Applied Science, Faculty of
Engineering, School of (Okanagan)
DSpace
UBCO
Batouei, Mohammad Amin
Master of Applied Science - MASc
University of British Columbia
Composite materials are essential in high-performance industries due to their superior strength-to-weight ratios and design versatility. However, ensuring consistent quality during their manufacturing processes, particularly in the curing stage, remains challenging. The autoclave curing process, involving heat and pressure application to prepreg materials, significantly impacts final mechanical properties. Unoptimized temperature, pressure, and heat transfer coefficient (HTC) selection can lead to considerable product quality deviations. This research explores using synthetic data and Artificial Neural Networks (ANNs), combined with thermal imaging and Haralick texture features, to predict and optimize autoclave curing outcomes. Unlike traditional off-line, simulation-based optimization techniques or experimental trial-and-error methods, the proposed approach enables near real-time control. Its application is demonstrated in three scenarios: (1) ideal conditions with no uncertainties in thermal camera measurements (prediction mode), (2) practical conditions accounting for measurement uncertainties (prediction mode), and (3) multi-objective design optimization using genetic algorithms (reverse engineering mode). SHapley Additive exPlanations (SHAP) sensitivity analysis was employed to quantify the contributions of input factors (Haralick features) to predictions. The results of the first scenario demonstrated that ANN could accurately predict the key process outcomes, such as degree of cure (DOC) and cure time, with an accuracy of > 94%. In the second scenario, the DOC prediction accuracy exceeded 96%, along with a prediction error of a maximum of 1% for the minimum degree of cure (DOC) outcome. Additionally, in both scenarios, the models successfully predicted the gel time and cure time with a maximum error of 6%. A sensitivity analysis, in the second scenario, further confirmed the model’s robustness, with performance remaining stable despite simulated temperature measurement uncertainties. Particularly, when the simulated measured temperature data was largely perturbed, the accuracy remained above 0.84 across all outcomes. In the third scenario, the ANN-guided approach, integrated with genetic algorithms, identified an optimal solution, estimating HTC values and balancing trade-offs between minimum DOC and cure uniformity. Aligned with Industry 4.0, this study highlights AI’s potential for improving on-site monitoring and process optimization in composite autoclave manufacturing through thermal imaging and feature extraction.
A FEASIBILITY STUDY ON ARTIFICIAL NEURAL NETWORK-BASED PREDICTION AND OPTIMIZATION OF AUTOCLAVE CURING PROCESS OUTCOMES VIA SIMULATION-BASED THERMAL IMAGES AND HARALICK TEXTURE FEATURES by Mohammad Amin Batouei B.Sc., IUST, Iran, 2022 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF APPLIED SCIENCE in THE COLLEGE OF GRADUATE STUDIES (Mechanical Engineering) THE UNIVERSITY OF BRITISH COLUMBIA (Okanagan) December 2024 © Mohammad Amin Batouei, 2024 ii The following individuals certify that they have read, and recommend to the College of Graduate Studies for acceptance, a thesis entitled: A feasibility study on artificial neural network-based prediction and optimization of autoclave curing process outcomes via simulation-based thermal images and Haralick texture features submitted by Mohammad Amin Batouei in partial fulfillment of the requirements of the degree of Master of Applied Science. Dr. Abbas S. Milani, School of Engineering Supervisor Dr. Pourang Irani, Department of Computer Science Supervisory Committee Member Dr. Solomon Tesfamariam, School of Engineering Supervisory Committee Member Dr. Shahria Alam, School of Engineering University Examiner Dr. Mahmudur Fatmi, School of Engineering Examination Chair iii Abstract Composite materials are essential in high-performance industries due to their superior strength-to-weight ratios and design versatility. However, ensuring consistent quality during their manufacturing processes, particularly in the curing stage, remains challenging. The autoclave curing process, involving heat and pressure application to prepreg materials, significantly impacts final mechanical properties. Unoptimized temperature, pressure, and heat transfer coefficient (HTC) selection can lead to considerable product quality deviations. This research explores using synthetic data and Artificial Neural Networks (ANNs), combined with thermal imaging and Haralick texture features, to predict and optimize autoclave curing outcomes. Unlike traditional off-line, simulation-based optimization techniques or experimental trial-and-error methods, the proposed approach enables near real-time control. Its application is demonstrated in three scenarios: (1) ideal conditions with no uncertainties in thermal camera measurements (prediction mode), (2) practical conditions accounting for measurement uncertainties (prediction mode), and (3) multi-objective design optimization using genetic algorithms (reverse engineering mode). SHapley Additive exPlanations (SHAP) sensitivity analysis was employed to quantify the contributions of input factors (Haralick features) to predictions. The results of the first scenario demonstrated that ANN could accurately predict the key process outcomes, such as degree of cure (DOC) and cure time, with an accuracy of > 94%. In the second scenario, the DOC prediction accuracy exceeded 96%, along with a prediction error of a maximum of 1% for the minimum degree of cure (DOC) outcome. Additionally, in both scenarios, the models successfully predicted the gel time and cure time with a maximum error of 6%. A sensitivity analysis, in the second scenario, further confirmed the model’s robustness, with performance remaining stable despite simulated temperature measurement uncertainties. Particularly, when the simulated measured temperature data was largely perturbed, the accuracy remained above 0.84 across all outcomes. In the third scenario, the ANN-guided approach, integrated with genetic algorithms, identified an optimal solution, estimating HTC values and balancing trade-offs between minimum DOC and cure uniformity. Aligned with Industry 4.0, this study highlights AI’s potential for improving on-site monitoring and process optimization in composite autoclave manufacturing through thermal imaging and feature extraction. iv Lay Summary This feasibility study develops a data-driven approach towards monitoring the quality of composite materials under the autoclave manufacturing process, which is being used particularly in high-tech industries such as aerospace. Even small variations of control parameters (e.g., heat and pressure) in this process are known to impact the final product’s quality sizably. Traditional monitoring methods for the curing process often lack immediate (real-time) feedback for on-site solutions. The current AI-inspired research attempts the use of machine learning in combination with simulated thermal imaging data to predict and optimize the curing outcomes. The results demonstrated that such AI-based models can effectively forecast crucial process factors such as curing time and the extent to which the material can be cured optimally. The approach could eventually help composite designers devise more time and cost-effective (automated) autoclave manufacturing routines for high-performance structures, as aligned with the emerging Industry 4.0 visions. v Preface This thesis, entitled 'A feasibility study on artificial neural network-based prediction and optimization of autoclave curing process outcomes via simulation-based thermal images and Haralick texture features' was carried out by Mohammad A. Batouei under the supervision of Dr. Abbas S. Milani at the Composites Research Network (CRN) Okanagan, within the School of Engineering at the University of British Columbia. The research topic was collaboratively identified by research fellows at the Materials and Manufacturing Research Institute (MMRI) and Dr. Milani as technical director of CRN, focusing on the feasibility of using thermal camera data captured during a simulated composite manufacturing process to predict post-process properties through machine learning techniques. This specific study served as a base case example to explore this concept, with the potential for broader application in other manufacturing processes in future. I was responsible for all simulations, numerical analyses, the development of machine learning models, and coding involved in this research. The thesis was written solely by me, with no external AI-generated content. AI was only utilized to refine sentence structure for clarity and precision. A summarized version of this thesis is being prepared for submission to a composites journal. Additionally, based on modeling techniques learnt, a collaborative work outside the immediate scope of this thesis was published during my Master’s program, as cited below: • O. H. Margoto, M. A. Batouei, Y. Abdin, and A. S. Milani, "Towards Enhancing Formability of Natural Fiber Reinforcements Using Data-Driven Approaches," in TEXCOMP-15 Conference, Leuven, Belgium, 2024, pp. 1-5. vi Table of Contents Abstract ......................................................................................................................................... iii Lay Summary ............................................................................................................................... iv Preface ............................................................................................................................................ v Table of Contents ......................................................................................................................... vi List of Tables ................................................................................................................................ ix List of Figures ................................................................................................................................ x List of Symbols ........................................................................................................................... xiv List of Abbreviations .................................................................................................................. xv Acknowledgements ................................................................................................................... xvii Dedication ................................................................................................................................. xviii Chapter 1: Introduction ............................................................................................................... 1 1.1 Background ...................................................................................................................... 1 1.2 Research Gap and Objectives ........................................................................................... 2 1.3 Thesis Framework ............................................................................................................ 3 Chapter 2. Literature Review ...................................................................................................... 5 2.1 Composite Curing Process ............................................................................................... 5 2.1.1 Methods..................................................................................................................... 5 2.1.2 Significance............................................................................................................... 7 2.2 Autoclave ......................................................................................................................... 8 2.2.1 Layout ....................................................................................................................... 8 2.2.2 Contributing Factors ............................................................................................... 10 2.2.3 Heat Transfer Coefficient ....................................................................................... 10 2.2.4 Output Variations .................................................................................................... 13 2.3 Curing Process Outcomes .............................................................................................. 14 2.3.1 Degree of Cure ........................................................................................................ 14 vii 2.3.2 Uniformity of Degree of Cure ................................................................................. 15 2.3.3 Cure Time ............................................................................................................... 16 2.3.4 Gel Time ................................................................................................................. 16 2.4 Machine Learning in Industrial Applications................................................................. 17 2.4.1 ML Applications to Predict Properties ................................................................... 17 2.4.2 A Synopsis of Machine Vision Applications .......................................................... 18 2.4.3 ML-Based Prediction of Process Outcomes in Autoclave Processing ................... 19 2.5 Summary ........................................................................................................................ 20 Chapter 3: Methodology............................................................................................................. 21 3.1 Case Study ...................................................................................................................... 21 3.1.1 Material and Process Descriptors Selected ............................................................. 21 3.1.2 Simulation in RAVEN for data generation ............................................................. 23 3.2 Defining Application Scenarios ..................................................................................... 26 3.2.1 Example 1 (Prediction Mode): Ideal Condition with No Uncertainty in Temperature Measurements .................................................................................................. 26 3.2.2 Example 2 (Prediction Mode): Mimicking Real-Life Implementation with Uncertainty in Temperature Measurements .......................................................................... 27 3.2.3 Example 3 (Reverse Engineering Mode): Multi-Objective Design Optimization.. 27 3.3 Time-series Heatmap...................................................................................................... 28 3.3.1 Global Grey Scale for Temperature Distribution.................................................... 29 3.3.2 Combining Camera Frames .................................................................................... 29 3.4 Haralick Features and Their Extraction ......................................................................... 30 3.4.1 Contrast ................................................................................................................... 33 3.4.2 Correlation .............................................................................................................. 33 3.4.3 Angular Second Moment (ASM) ............................................................................ 34 3.4.4 Energy ..................................................................................................................... 34 viii 3.4.5 Homogeneity ........................................................................................................... 34 3.4.6 Dissimilarity ............................................................................................................ 35 3.4.7 Haralick Features vs. Process Outcomes: Hypotheses ........................................... 35 3.5 Machine Learning (ML) Techniques ............................................................................. 35 3.5.1 Recap: General Context of ML ............................................................................... 36 3.5.2 Artificial Neural Network (ANN) ........................................................................... 36 3.6 Summary ........................................................................................................................ 41 Chapter 4: Results and Discussion ............................................................................................ 44 4.1 Example 1: Ideal Condition............................................................................................ 44 4.1.1 Results ..................................................................................................................... 45 4.1.2 Discussion ............................................................................................................... 51 4.2 Example 2: Mimicking Real-Life Implementation with Uncertainties in Thermal Measurements ........................................................................................................................... 53 4.2.1 Prediction of Process Outcomes ............................................................................. 55 4.2.2 Past 2: Adding Noise to the Data and Robustness Assessment .............................. 61 4.2.3 Discussion ............................................................................................................... 65 4.3 Example 3: Multi-Objective Design Optimization ........................................................ 66 4.3.1 ANN Model to Predict HTCs ................................................................................. 67 4.3.2 Optimization Problem ............................................................................................. 71 Chapter 5: Conclusions, Limitations, and Future Work Recommendations ........................ 80 5.1 Conclusions .................................................................................................................... 80 5.2 Limitations and Future Work Recommendations .......................................................... 84 Bibliography ................................................................................................................................ 87 ix List of Tables Table 1. Summary of Case Study Process Parameters Fixed ....................................................... 23 Table 2. The ANN models’ architecture for each output in an ideal condition scenario (example 1) ................................................................................................................................................... 48 Table 3. The ANN model’s description for each output in Example 2 (part 1) ........................... 56 Table 4. Comparing the models’ performance on test datasets in Examples 1 and 2 .................. 66 Table 5. The ANN models description for the prediction of HTC values ................................... 68 Table 6. The result of TOPSIS MCDM on Pareto front solutions in Example 3 ........................ 77 Table 7. Comparing results of NSGA-II-TOPSIS with the corresponding samples through ground-truth simulations in Example 3......................................................................................... 78 x List of Figures Figure 1. General schematic of an autoclave [52] ......................................................................... 9 Figure 2. Schematic of the inputs and curing in an autoclave (Redrawn from [67]) ................... 13 Figure 3. Schematic of thermoset resin curing stages: starting from (A)uncured monomers, moving to (B)increasingly viscous material before gelation, then to (C)gelled but not fully crosslinked network, and finally to (D)fully cured thermoset resin [69] ...................................... 17 Figure 4. A liver sample with the found Region of Interest (ROI) to extract textural features [93]....................................................................................................................................................... 19 Figure 5. Visual Description of The Case Study and the Chosen Cure Cycle. ............................ 23 Figure 6. (a) DOC comparison between RAVEN simulations and dielectric analysis (b) Correlation of RAVEN-simulated DOC with experimental results from multiple dielectric parameters [102]. .......................................................................................................................... 25 Figure 7. Custom Grey Scale for Temperatures .......................................................................... 29 Figure 8. An Example of the Time-series Heatmap with its Scale on the Side ........................... 30 Figure 9. An Example of how GLCM is constructed [125] ......................................................... 33 Figure 10. ReLU activation function (implemented in this study) .............................................. 38 Figure 11. Visualization of overfitting and underfitting in loss curve graph [144] ..................... 39 Figure 12. Summary of the Methods and Workflow used in this Study ..................................... 43 Figure 13. The Custom Global Grey Scale used for Ideal Condition Scenario ........................... 44 Figure 14. An Example of the Time-series Heatmap Used in an Ideal Condition Scenario ....... 45 Figure 15. Pairwise Correlation Heatmap Between the Haralick Features in Example 1 ........... 47 xi Figure 16. Loss function curves during ANN training for different process outcomes in Example 1: (a) Min DOC, (b) Uniformity factor of DOC through thickness, (c) Gel time, and (d) Cure time ............................................................................................................................................... 48 Figure 17. Absolute error plots for ANN after training for different process outcomes in Example 1: (a) Min DOC, (b) Uniformity factor of DOC through thickness, (c) Gel time, and (d) Cure time ....................................................................................................................................... 49 Figure 18. Residual plots for ANN after training for different process outcomes: (a) Min DOC, (b) Uniformity factor of DOC through thickness, (c) Gel time, and (d) Cure time ...................... 49 Figure 19. R-squared plots for different process outcomes’ ANNs in Example 1: (a) Min DOC, (b) Uniformity factor of DOC through thickness, (c) Gel time, and (d) Cure time ...................... 50 Figure 20. Bar plots of SHAP values for different process outcomes’ ANNs in Example 1: (a) Min DOC, (b) Uniformity factor of DOC through thickness, (c) Gel time, and (d) Cure time .... 51 Figure 21. Correlation heatmap between input features and process outcomes in Example 1 .... 52 Figure 22. The custom global grey scale used for Example 2 ..................................................... 54 Figure 23. An example of the time-series heatmap in Example 2 ............................................... 54 Figure 24. Pairwise Correlation Heatmap Between Haralick Features in Example 2 (part 1) .... 56 Figure 25. Loss function curves during ANN training for different process outcomes in Example 2 (part 1): (a) Min DOC, (b) Uniformity factor of DOC through thickness, (c) Gel time, (d) Cure time, and (e) Temperature overshoot ............................................................................................ 57 Figure 26. Absolute error plots for ANN after training for different process outcomes in Example 2 (part 1): (a) Min DOC, (b) Uniformity factor of DOC through thickness, (c) Gel time, (d) Cure time, and (e) Temperature overshoot.............................................................................. 58 xii Figure 27. Residual plots for ANN after training for different process outcomes in Example 2 (part 1): (a) Min DOC, (b) Uniformity factor of DOC through thickness, (c) Gel time, (d) Cure time, and (e) Temperature overshoot ............................................................................................ 59 Figure 28. R-squared plots for different process outcomes’ ANNs in Example 2 (part 1): (a) Min DOC, (b) Uniformity factor of DOC through thickness, (c) Gel time, (d) Cure time, and (e) Temperature overshoot ................................................................................................................. 60 Figure 29. Bar plots of SHAP values for different process outcomes’ ANNs in Example 2: (a) Min DOC, (b) Uniformity factor of DOC through thickness, (c) Gel time, (d) Cure time, and (e) Temperature overshoot ................................................................................................................. 61 Figure 30. Comparison of the time-series heatmap before and after adding temperature errors in Example 2 ..................................................................................................................................... 63 Figure 31. R-squared plots for different process outcomes’ ANN with uncertainties added in Example 2: (a) Min DOC, (b) Uniformity factor of DOC through thickness, (c) Gel time, (d) Cure time, and (e) Temperature overshoot ................................................................................... 64 Figure 32. Loss function curves during ANN training for predicting HTC values ..................... 68 Figure 33. Absolute error plots for ANN after training for predicting HTC values: (a) Top-side HTC, and (e) Bottom-side HTC .................................................................................................... 69 Figure 34. Residual plots for ANN after training for predicting HTC values: (a) Top-side HTC, and (e) Bottom-side HTC.............................................................................................................. 69 Figure 35. R-Squared plots of predicting HTC values: (a) Top-side HTC, and (e) Bottom-side HTC............................................................................................................................................... 69 Figure 36. The plots of average temperatures at the top and bottom compared to their respective HTC values ................................................................................................................................... 70 xiii Figure 37. Pairwise correlation heatmap between the process outcomes for samples within the optimization constraints ................................................................................................................ 73 Figure 38. Pairwise comparison between min DOC and the rest of process outcomes for samples within the optimization constraints ............................................................................................... 73 Figure 39. Pareto front solutions created by the genetic algorithm for Example 3 (for numbered/ranked points, refer to Table 6) .................................................................................... 76 xiv List of Symbols h heat transfer coefficient A surface area ∆Ts temperature difference between autoclave environment and composite material ?̇?𝑠 rate of the change of the surface temperature of the sensor ?̇? rate of heat transfer 𝜌𝑐 sensor density 𝐶𝑃𝑐 sensor specific heat capacity 𝑉 sensor volume 𝛼(𝑡) degree of cure at time 𝑡 ?̇?𝑒𝑥 rate of heat release ∆𝐻𝑡𝑜𝑡𝑎𝑙 total heat of reaction for complete curing 𝑠 standard deviation xv List of Abbreviations AI Artificial Intelligence ANN Artificial Neural Network ASM Angular Second Moment CNN Convolutional Neural Network CRN Composites Research Network CV Coefficient of Variation DMA Dynamic Mechanical Analysis DNN Deep Neural Network DOC Degree of Cure DSC Differential Scanning Calorimetry FCNN Fully Connected Neural Network GA Genetic Algorithm GLCM Gray-Level Co-occurrence Matrix HTC Heat Transfer Coefficient HVAC Heating, Ventilation, and Air Conditioning IoT Internet of Things KNN K-Nearest Neighbors LBP Local Binary Pattern LPBF Laser powder bed fusion MAE Mean Absolute Error MCDM Multi-Criteria Decision Making ML Machine Learning MRCC Manufacturer’s Recommended Cure Cycle MRI Magnetic Resonance Imaging MSE Mean Squared Error NSGA-II Non-dominated Sorting Genetic Algorithm II OOA Out of Autoclave PET Polyethylene Terephthalate PFRC Polyethylene Terephthalate Fiber-Reinforced Concrete xvi PID Proportional Integral Derivatives PLC Programmable logic controllers PSA Pressure Swing Adsorption RBF Radial Basis Function ReLU Rectified Linear Unit ROI Region of Interest RSM Response Surface Methodology SBX Simulated Binary Crossover SHAP SHapley Additive exPlanations SVM Support Vector Machine SVR Support Vector Regression TGA Thermogravimetric Analysis TOPSIS Technique for Order Preference by Similarity to Ideal Solution UV Ultraviolet xvii Acknowledgements I am very glad to have met many great people during my MASc program at UBC, who have made this journey highly special. It has been a pleasure to work and interact with these outstanding individuals. First and foremost, I want to thank my supervisor, Dr. Abbas S. Milani, for his immense help and support throughout my program. I consider myself very fortunate to have been able to work with him. I have seen many examples of his great character, not the least of which is treating his students with the utmost respect and kindness. I would like to thank all my colleagues and friends at CRN Okanagan, who have helped me throughout my research journey. I am glad to have been a part of this great team and worked on different projects. I would also like to thank Dr. Milad Ramezankhani, Dr. Bryn Crawford, and Mostafa Sharifan for their technical support. I would like to especially thank Ms. Nadia Abdollahi and Ms. Olivia Margoto, who have been nothing but supportive as my great friends every day in the lab. Lastly, I want to express my sincere gratitude to my family and girlfriend for always being there for me with their unconditional love. They gave me the strength to go through this journey, and this work would not have been possible without their support. I appreciate all your endless help, and I love you from the bottom of my heart. xviii Dedication …To the people I love the most and who are dearest to my heart—those who bring a smile to my face whenever I think of them… Chapter 1: Introduction 1.1 Background Composite materials have already become a cornerstone of high-performance manufacturing industries such as aerospace, automotive, and construction, owing to their superior strength-to-weight ratios and the ability to adapt to specific design requirements in each application [1], [2], [3]. While offering these benefits, they also require careful monitoring and control throughout their manufacturing processes to ensure the quality and reliability of final products [4], [5]. Manufacturing composite materials is a multi-billion-dollar industry sector today [6]. The curing process is one of the critical stages in the manufacturing of thermosetting composite structures. The curing transforms the composite resin/fibre material system from a pliable state into a rigid form, while significantly affecting its final mechanical properties and performance during service [7]. For high-quality industrial parts, this process typically occurs in an autoclave, where heat and pressure are applied to the given composite layup [8], [9]. The control parameters of this process, such as the applied temperature, pressure, and heat transfer coefficient, are vital and unoptimized variations in these parameters can lead to significant deviations from the ideal structural properties initially designed [10]. Given the cost of each optimization trial and the number of factors affecting the autoclave curing process, there is a prospect in the literature for implementing advanced, yet often cost-effective AI-based monitoring and control techniques [10]. Artificial Neural Networks (ANN) are one such model inspired by the network of neurons in human brains [11]. ANNs have showcased their significant potential across various application fields with their ability to decode trends within sets of data points. They are trained using historical data and make predictions for unseen/future data [12]. Combining AI and machine vision for manufacturing process monitoring and improvement is deemed both innovative and practicable [13]. This integrated approach allows e.g. simple camera-based monitoring and immediate feedback after each design trial, enabling manufacturers to make swift adjustments to their process parameters. The eventual benefit of this approach would be to reduce time and experimental costs during new production cycles, where the quality and reliability of parts are essential for safety and performance during service. Such AI-based smart manufacturing, also known as Industry 4.0, generally represents the use of advanced digital technologies, robotics, the Internet of Things (IoT), data analytics, immersive technologies, etc., into complex systems [14]. The approach chosen to monitor the composite's autoclave curing 2 process aligns with the Industry 4.0 principles, implementing what potentially can be a more intelligent, automated, and self-optimizing production system for aerospace industries, among others [14]. 1.2 Research Gap and Objectives Variations in curing process parameters are known to lead to sizable outcomes in the properties of the cured composite material systems, potentially compromising the performance and safety in the final product [10]. For instance, the degree of cure (DOC) is known to be one of the crucial process outcomes that affects the strength and durability of the prepreg-based composites. Insufficient DOC results in weak local areas within the part, causing failures when the product is used, e.g., under extreme loading conditions in service [15], [16]. Traditional simulation-based or trial-and-error experimental methods used for monitoring the composites autoclave curing process often lack the feature to give fast feedback as necessary for adjustments by operators on-site, and at the same time they are not always cost-effective [17]. In addition, lab-based material characterization techniques used during experimental trials, such as differential scanning calorimetry (DSC), rheology, dynamic mechanical analysis (DMA), and thermogravimetric analysis (TGA), are generally applied to small-size samples within a controlled lab setting, making them challenging to adapt for real-time cure monitoring of ‘large’ composite parts without extensive hardware alterations [18]. As a result, there is a clear need/gap for data-driven techniques that can possibly provide more continuous/nearly real-time and accurate monitoring of the curing process, along with subsequent process adjustments, eventually ensuring that the process's output meets the required design criteria. Towards addressing the above gap, the overall objective of this study is to explore the potential of ANNs using simulated thermal camera images and associated Haralick texture features [19], for prediction and optimization of critical process outcomes in a composite autoclave curing. Simulations in engineering often serve as a practical and scalable means to explore complex proof of concepts, enabling a detailed examination of their feasibility without the prohibitive time and cost constraints of experimental setups. Given that real-life autoclave testing of large parts is highly time-consuming and costly, process simulations here represent an essential phase of the AI-based feasibility study. By accurately predicting the degree of cure and other critical process outcome metrics, such a data-driven model would enable direct adjustments to the 3 process parameters on-site while ensuring design constraints are met. The sub-objectives/tasks of the research are as follows: 1. Demonstrating the feasibility of the selected AI-based approach through a case study for predicting outcomes/metrics of the autoclave process, such as cure time, gel time, DOC, and its corresponding uniformity factor. 2. Assessing the robustness of the AI-based model against thermal camera measurement uncertainties that may occur in practice (including incomplete and noisy datasets). 3. Using a multi-objective genetic algorithm that leverages the trained ANN models (as surrogate models) for process design optimization purposes. In this case, two design outcomes will be optimized concurrently (as a multi-objective problem): uniformity factor and DOC. Remark: The above three sub-objectives will be named as examples 1-3 throughout the case study in the rest of this thesis. 1.3 Thesis Framework The rest of this document will continue with a detailed literature review in Chapter 2, which provides an overview of various composite curing methods, emphasizing their significance in the high-tech composites manufacturing sector. The chapter then narrows its focus to autoclave curing, the core subject of this research. It covers a general overview of the process, describing its components and the key factors influencing the process outcomes. Additionally, the chapter highlights how these factors can affect the final results. The chapter concludes by exploring examples of previous research on the application of machine learning techniques similar to those used in this study in industrial contexts. Chapter 3 shifts focus to the AI methodology proposed, including a description of the selected case study. It also outlines example implementation scenarios where the proposed method could be applied with slight modifications or for different purposes. The chapter then further dissects each core step of the methodology, including the preprocessing of data sampling from the case study to train the ANN models. Then, finally, a review of the machine learning techniques is provided. Chapter 4 presents the results while discussing the key points of each scenario. It emphasizes the differences between the three scenarios and their specific objectives. 4 Finally, Chapter 5 delivers the main conclusions of the work, addressing each implementation scenario. The chapter closes by discussing the limitations encountered during the research and proposing recommendations for future work. 5 Chapter 2. Literature Review 2.1 Composite Curing Process Composite materials are now integral to the manufacture of products across diverse industries seeking high-performance/lightweight structures [20]. A typical composite material is composed of a reinforcing phase (e.g., fibres) placed within a polymer matrix, which binds the reinforcing phase together, passing loads from one to the other and giving the composite its shape and structural integrity [21], [22], [23]. Furthermore, one of the most important processes in composite manufacture is curing. This transformation from liquid or in-gel state in the polymer matrix to solid state is the key to enabling the desirable properties of the composite, such as its mechanical strength, thermal stability and durability [24], [25]. It also influences the microstructure of the composite, as it affects void content and the extent of the degree of polymerization [26], [27]. Different curing methods are used to complete this task depending on the application and a given composite material. Some methods can be classified in the same category; however, due to differences in the curing process, they result in distinct outputs. Overall, there are numerous ways to cure raw composite materials, and each has its own advantages, so it depends on the product to decide which method should be used. 2.1.1 Methods The most common method of curing composites is thermal curing, which involves heating the composite to undergo the chain reactions that set the polymer matrix, as opposed to e.g., UV-based curing. Typically, thermal curing is carried out in either an oven or an autoclave [28]. Autoclave curing is common within high-performance sectors such as aerospace because the confined environment of an autoclave ensures composites meet a high standard with minimal voids or other imperfections. The application of pressure ensures even heating throughout the composite, which in turn helps the chemical processes to proceed smoothly throughout [26], [29]. However, thermal curing is energy-intensive and requires significant investment in both equipment and operational costs due to the prolonged curing cycles necessary for thorough and uniform curing of the composite material [30]. The energy required for autoclave curing is estimated to be between 20-22 MJ/kg, comprising less than 10% of the overall energy consumption in the manufacturing process. However, this is still a considerable amount of energy that is required for the process [31]. 6 Out of Autoclave (OOA) curing techniques, like vacuum bagging, resin transfer moulding, and prepreg moulding, are designed to bypass the necessity to use autoclaves [32]. Achieving this goal results in reducing costs and increasing the ability to produce on a larger scale [33]. As a result, being able to implement them for cases like wind turbine blades where the use of large autoclaves is impractical [34]. OOA methods enable the production of large composite structures with reduced energy consumption and shorter cycle times [33]. One other type of curing can be mentioned as radiation curing, which consists of methods such as electron beam, gamma ray, and ultraviolet (UV) curing, offering an alternative to traditional thermal methods [35]. Generally, this method uses high-energy radiations to initiate the process of polymerization and crosslinking in the polymer matrix [36], [37]. One of the advantages is a quicker curing process while reducing the required energy to perform the process. It can be done at room temperature, making it particularly beneficial for temperature-sensitive applications [36], [38]. However, the downsides are the high cost of the needed equipment and the safety concerns that come with such high-energy emissions [39]. Below is a more detailed explanation of the processes that all fall under this type of curing. Electron beam curing uses accelerated electrons to start polymerization, allowing for rapid curing and deep penetration into thick composite sections [40]. This is considered an advantage for environments where high-speed production is key while the material is thick. Gamma-ray and X-ray curing methods can also penetrate very thick composites, making them suitable for scenarios similar to electron beam curing. Despite this, these two methods are less commonly used due to long curing times and significant safety and disposal issues associated with radioactive materials, on top of the high cost of equipment for all of them [36], [41]. Furthermore, UV curing is commonly used for surface coatings and thin composite layers due to its energy efficiency and being more environmentally friendly [42], [43]. UV light induces quick polymerization and crosslinking also at room temperature [44]. However, it has limited applications like transparent composites or surface layers because of the inability of the UV light to have high penetration depth [45], [46]. Therefore, as an innovation, UV curing is used as a hybrid approach by combining it with thermal curing to cover its shortcomings [47]. 7 2.1.2 Significance The significance of the composite curing process is not only about the material transformation its impact on performance. It also matters for the cost feasibility and reliability of composite materials. The cost is a significant factor in the overall economics of composite manufacturing. Autoclave curing, despite its ability to produce high-quality products, is capital-intensive due to the high costs associated with equipment and energy consumption [30]. Therefore, any attempts at reducing the number of errors and trials to get to the desired quality can save significant cost, energy and time. As mentioned, although radiation curing methods are potentially more energy-efficient, they require expensive equipment and strict safety measures, increasing the overall cost [39]. The feasibility and scalability of different curing methods depend on factors such as the size and complexity of composite parts, production volume, and the infrastructure [48]. For instance, autoclave curing is a great choice for high-performance and any size of parts but becomes a bit impractical for very large parts due to the cost and energy that it requires [49]. Therefore, based on the scenario, OOA techniques and radiation curing methods that provide scalable solutions that can accommodate larger parts can be implemented. As such, the wind energy industry benefits from OOA methods for manufacturing large turbine blades [34]. Environmental impact and safety are also critical considerations in the selection of curing methods. Thermal curing processes consume considerable amounts of energy and higher carbon emissions [49]. While radiation curing methods are potentially more energy-efficient, they pose safety risks due to the use of high-energy radiation and the creation of hazardous by-products. However, UV curing is considered more environmentally friendly, as it requires lower energy levels and does not produce harmful emissions, but it has limited use [35], [42]. All things considered, it is essential to focus on proper curing since getting the optimal crucial mechanical properties, such as tensile strength and stiffness, correlated to proper curing [7]. The process affects the formation of a robust fiber-matrix interface and the degree of polymerization to withstand mechanical loads and stresses [27]. Additionally, reliable and uniform curing minimizes defects such as voids and incomplete polymerization, enhancing structural integrity and reliability [7], [26]. Durability is also significantly impacted by the curing process, with well-cured composites showing improved resistance to environmental factors like moisture 8 and temperature fluctuations, as well as UV radiation [50]. This ensures longevity and consistent performance of composite materials in their end-use applications. In conclusion, the curing process is a critical determinant of the final properties of the process outcomes. However, the choice of curing method is influenced by a combination of performance requirements, cost constraints, scalability, environmental considerations, and application-specific needs. As a result, it is up to manufacturers to make that decision based on the specific application and specifications of the final product. 2.2 Autoclave Despite the various advanced curing methods that were mentioned, the autoclave curing process remains the common practice in the composite manufacturing industry [51]. Having the ability to provide a highly controlled environment makes it the popular option for ensuring high-quality products. This section delves into detailing its layout, contributing factors to the curing process, the critical role of the Heat Transfer Coefficient (HTC), and the variations in output that can happen. 2.2.1 Layout A typical industrial autoclave is a complex system designed to help cure composite materials effectively. The main components and their functions can be described below: • Heating System: It is very clear that the heating system is the Integral part of the autoclave. This part typically consists of electrical heating elements. These heaters are strategically placed to ensure uniform heat distribution throughout the vessel [52], [53]. The control of these heaters is key for maintaining the exact temperatures required for different curing cycles. • Air Circulation System: The air circulation system consists of powerful fans or blowers, which are essential for maintaining uniform temperature and pressure. These fans ensure that the heated air is evenly distributed around the composite, preventing the formation of hotspots and ensuring a consistent curing process [52], [54]. • Control System: Modern autoclaves have advanced control systems that monitor and change parameters such as temperature, pressure, and timing. These parameters collectively form a cure cycle. Often, these systems are built with programmable logic 9 controllers (PLCs) and proportional integral derivatives (PIDs) that can implement curing schedules to ensure repeatability and precision [52], [54]. • Material Placement: Inside an autoclave, composite materials are placed on specially designed racks or shelves called loading platforms. These placements are in a way that can maximize the effect of airflow and heat distribution around each part [54]. The part that is supposed to be cured is usually placed on top of another material, which is called tooling. It helps with keeping the shape and alignment of the composite part, creating uniform pressure distribution during the curing process [55]. • Cooling System: Once a curing cycle is done, the device has to cool down in a controlled way to prevent any thermal shocks that could damage the composite material. This is typically achieved using a combination of natural cooling and active cooling systems. The active cooling system includes parts like heat exchangers, which help regulate the temperature by moving heat away from the autoclave. The cooling system often uses both air and water as cooling media, which can be controlled to have an effective cooling rate [52], [54]. • Pressurization: Modern autoclaves often use nitrogen instead of air to lower the risk of fire for pressurization. The system has a main compressor that pressurizes air, a nitrogen plant that isolates nitrogen using Pressure Swing Adsorption (PSA) technology, and a booster compressor that further pressurizes the nitrogen. Creating an environment with consistent pressure is essential for the proper curing of composite materials [52], [54]. In Figure 1 an ordinary autoclave can be seen with all the parts that were mentioned in this section. Figure 1. General schematic of an autoclave [52] 10 2.2.2 Contributing Factors Several important factors during the autoclave curing process heavily control the quality of the final cured composite part. Each factor should be closely controlled to achieve the desired outcomes. • Pressure and Vacuum Application: Putting high pressure on the composite part and the use of vacuum bags during the curing process helps to keep the layers together, removing voids and trapped air to make a dense, uniform structure. It also helps with the removal of excess resin, which can have a negative impact on the material’s mechanical property [56], [57]. • Temperature: The temperature profile should be controlled to start and continue the polymerization reaction in the resin matrix. The temperature ramps up slowly to avoid thermal gradients that result in uneven curing [58]. • Time: Another important factor is the duration of the curing process. Enough time has to be put into ensuring the polymerization and cross-linking of the resin are complete. If the curing time is not sufficient, it can result in a partially cured composite with inferior mechanical properties [59]. The combination of these last factors is what the cure cycle is determined from. • Heat Transfer Coefficient (HTC): HTC is the main factor affecting the efficiency of heat transfer from the autoclave environment to the composite material. A higher HTC ensures more uniform and effective heat transfer, which is important to achieve consistent curing throughout the material [60]. • Size and Material of the Part: The dimensions and material composition of the composite parts make an impact on the curing process. Larger parts or materials with lower thermal conductivity may need longer curing times and a more challenging control of the heating and cooling rates for a uniform temperature distribution [61], [62]. 2.2.3 Heat Transfer Coefficient The Heat Transfer Coefficient (HTC) measures the efficiency of heat transfer between the autoclave environment and the composite material. Understanding and controlling HTC, to a degree, is crucial for a uniform curing profile, which leads to achieving the desired material properties. HTC is very important because it determines how effectively heat is transferred to the composite material. A higher HTC indicates more efficient heat transfer, leading to a more uniform 11 and efficient curing. This is particularly important for thick or complex parts, where heat penetration must be thorough to ensure consistent curing throughout the material [60]. At very low HTCs, there is a high thermal resistance at the boundary between the autoclave environment and the composite material. This results in insufficient heat transfer to the composite, causing less uniform curing. The high boundary resistance limits the flow of heat, which leads to depending more on the exothermic reaction within the composite to generate the necessary heat for curing. Since the cross-linking reaction of the resin is an exothermic reaction which generates heat [63], [64]. However, this can lead to temperature overshoots, where localized areas within the composite become excessively hot since the heat is trapped inside. These overshoots can cause uneven curing and thermal degradation [61]. In contrast, a high HTC indicates low thermal resistance at the boundary, allowing for efficient heat transfer from the autoclave environment to the composite. Reducing the dependence on the exothermic reaction within the material means less trapped heat inside since the energy dissipates easier from the boundaries. While this minimizes the risk of temperature overshoots, it also means that the exothermic heat generated has a less significant role. If not properly managed, this can lead to under-curing, especially if the external heat source is not enough to maintain the necessary temperatures throughout the curing cycle [63], [64]. The optimal HTC is a balance that ensures effective heat transfer without resulting in overheating or under-curing. At a balanced HTC, the heat transfer is efficient enough to use both external heating and the exothermic reaction in the part itself. However, achieving this balance is challenging due to the variability of HTC within an autoclave [63], [64]. Furthermore, HTC impacts the curing time of composite materials in an autoclave. The relationship between HTC and curing time is non-monotonic, meaning it does not follow a simple linear pattern. Initially, as HTC increases from a very low value, the curing time decreases because the heat transfer to the composite material becomes more efficient. However, beyond a specific range, an increase in HTC leads to marginally longer curing times. This is because the same phenomenon explained makes the effect of exothermic reactions less significant. When the HTC is too high, the external heat transfer dominates, potentially causing the surface to cure faster than the interior, resulting in uneven curing and defects. At the same time, it takes more time for some parts within the composite to get to the cured state, therefore increasing the cure time [64]. 12 The calculation of HTC involves determining the rate of heat transfer between the autoclave environment and the composite material. This can be expressed using the formula [63]: ?̇? = ℎ ∙ 𝐴 ∙∆𝑇𝑠 (1) where: - ?̇? is the rate of heat transfer (W), - ℎ is the heat transfer coefficient (𝑊𝑚2𝐾), - 𝐴 is the surface area (m²), - ∆𝑇𝑠 is the temperature difference between the autoclave environment and the composite material (K). In practical terms, HTC can be measured with sensors experimentally using lumped mass calorimeters (no temperature gradient) to measure the other values within that sensor in order to calculate the HTC. To calculate the rate of heat that the mass absorbs, the formula below can be used [64]. ?̇? = 𝜌𝑐 ∙ 𝐶𝑃𝑐 ∙ 𝑉 ∙ ?̇?𝑠 (2) where: - 𝜌𝑐 is the sensor density (kg/m3), - 𝐶𝑃𝑐 is the sensor-specific heat capacity (J⋅kg−1⋅K−1), - 𝑉 is the sensor volume (m3), - ?̇?𝑠 is the rate of the change of the surface temperature of the sensor (K/s), Looking closer at the formulas above, it’s clear that factors such as material properties, part geometry, surface condition, and the autoclave itself affect the HTC value. The pressure and airflow within the autoclave influence HTC since it affects heat transfer [65]. Therefore, the value of HTC is not the same throughout the whole autoclave environment. This means that for any specific parts of the composite material, there will be two different values of HTC at the top-side and bottom-side, since the conditions at those parts may not be similar [66]. At the same time, it leads to spatial variability in HTC within the autoclave, which can result in differences in heat transfer rates across different areas of the composite part. Considering all factors, the value of HTC can vary widely (e.g., 10-200 𝑊𝑚2𝐾)) which with the effect that it has on 13 the curing process, shows the importance of being able to monitor the outputs of the process [60], [64]. Knowing all this information, the schematics of the inputs and the curing itself in an autoclave can be shown in Figure 2. Figure 2. Schematic of the inputs and curing in an autoclave (Redrawn from [67]) 2.2.4 Output Variations While most of the mentioned contributing factors in the autoclave curing process can be precisely controlled, HTC introduces a level of uncertainty due to its variability. This section discusses how these variations affect the final output and the measures that can be taken to control their impact. Factors such as pressure and vacuum application, temperature, and time are controllable. These parameters can be accurately controlled using the autoclave’s advanced control systems [52], [54]. By following a carefully designed cure cycle, consistent results can be achieved for these factors. However, HTC and its variability, as mentioned, is hard to precisely control and set to a value because a combination of many factors determines that [63], [64]. Unlike other factors, HTC is influenced by several dynamic conditions, making it challenging to control precisely. As mentioned, HTC has different values within the space of an autoclave, even having a difference factor between 1.3 and 3 comparing the front and back (depending on the Reynolds number of air flow) of the autoclave itself [60]. Variability in HTC can lead to differences in heat transfer rates, causing uneven curing and variations in the final material properties [64]. Advanced monitoring and control systems can be used to address these variations. The intention of this research work is to leverage an innovative approach to monitor and predict the outputs of the curing process. 14 2.3 Curing Process Outcomes As previously mentioned, the curing process of composite materials is very important in determining the final properties and performance of the composite parts. This process changes the raw composite into robust, durable structures able to withstand their designed operational conditions. The efficiency of the curing process directly influences the mechanical properties and overall quality of the composite [7], [27]. This section talks about the main outcomes of the curing process, including the degree of cure, its uniformity, cure time, and gel time. 2.3.1 Degree of Cure The degree of cure (DOC) is the extent to which the resin matrix in a composite material has gone through chemical reactions to shift from a liquid or semi-solid state to a solid state. It is shown with a value between 0 to 1 (or 0% to 100%), with the latter meaning a fully cured resin [68]. As shown in Figure 3, with the progression of the curing process, the formation of a crosslinked network happens, resulting in an increase in the DOC value. The final DOC is affected by the thermal history of the resin during the curing process. This is because the number of chemical crosslinks formed in the resin increases over time, as shown, pushing the DOC value closer to 1 [68], [69]. It is important to note that a resin does not need to achieve a DOC of 1 to be considered solid and functional. Depending on the case use, a thermoset resin like epoxy is regarded as fully cured when the DOC reaches around 0.85-0.9 or higher [64], [70]. The degree of cure can be quantified by measuring the amount of heat released during the curing process, known as the exothermic heat of reaction. This can be written mathematically using differential scanning calorimetry (DSC) data as follows [71], [72]: 𝛼(𝑡) =∫ ?̇?𝑒𝑥(𝑡) 𝑑𝑡𝑡0∆𝐻𝑡𝑜𝑡𝑎𝑙 (3) where: - 𝛼(𝑡) is the degree of cure at the time 𝑡, - ?̇?𝑒𝑥 is the rate of heat release at the time 𝑡 (W), - ∆𝐻𝑡𝑜𝑡𝑎𝑙 is the total heat of the reaction for complete curing (J), The degree of cure varies throughout the composite part due to several factors affecting heat transfer, such as HTC, as mentioned before. Meaning that there will be maximum and 15 minimum values for DOC in a composite part [73]. Achieving a uniform degree of cure is essential for consistent material properties across the part. An optimal DOC means that the material has reached close to its maximum potential in terms of strength and durability [70]. However, an incomplete cure can lead to defects, residual stresses, and reduced performance, showing that DOC is a critical parameter in the manufacturing process of composite materials [26]. 2.3.2 Uniformity of Degree of Cure The uniformity of the degree of cure within a composite part calculates how consistently the material has cured all around its volume. This is very important, especially for thick composite parts, where alterations in factors of heat transfer may lead to less uniform curing. Non-uniform DOC can result in areas with different mechanical properties, potentially resulting in weak spots and premature failure under load [74]. To assess the uniformity of the degree of cure, measurements of it at various points through the thickness of the composite part can be used. The coefficient of variation (CV) is a useful metric for this purpose, it is the ratio of the standard deviation to the mean of the DOC values measured [75]: 𝐶𝑉 =√ 1𝑛 − 1∑ (𝑦𝑖 − ∑ 𝑦𝑖𝑛𝑖=1𝑛)2𝑛𝑖=1∑ 𝑦𝑖𝑛𝑖=1𝑛= 𝑠?̅? (4) where: - 𝑦1, 𝑦2, … , 𝑦𝑛 are different measurements of DOC, - 𝑛 is the number of measurements taken, - 𝑠 is the standard deviation of the measurements, - ?̅? is the average of the taken measurements, A low CV indicates a more uniform degree of cure, while a higher CV shows more variation. Based on this literature definition, a lower uniformity factor value is desired in the composites design practice [76], [77]. Also, the uniformity factor may also be calculated using the difference between the maximum and minimum values of DOC [78]. While many factors such 16 as HTC affect uniformity, making sure of a more uniform cure is key to reach the intended performance of the composite material and prevent distortions [79]. 2.3.3 Cure Time The amount of time needed for a composite item to reach a cured state is referred to as the cure time. This is commonly described as the amount of time required for the part’s minimum DOC to surpass a particular threshold, signifying that the material has attained the desired characteristics [64]. Cure time is a crucial component of the manufacturing process since it has a direct impact on energy usage, production efficiency, and process cost overall. Shorter cure periods can lead to increased productivity and lower manufacturing costs [31]. This must be balanced alongside the requirement for sufficient curing in order to guarantee that the composite reaches its full mechanical and thermal potential. Numerous variables, such as the curing temperature, pressure, HTC, and composite element thickness, impact the cure time [80]. While higher pressures and temperatures might quicken the curing process, they also raise the possibility of defects and thermal overshoot [81]. Therefore, to find the appropriate balance between cure duration and quality, careful monitoring of the curing conditions is needed. 2.3.4 Gel Time The time it takes for the resin in a composite material to transform from a liquid or semi-liquid to a rubbery or gel state is known as the gel time [82], [83]. As shown in Figure 3 (c), The resin network reaches the point of transition when there is enough cross-linking for it to function like a solid [69]. Because it marks the beginning of major changes in the resin’s mechanical characteristics, gelation is an important step in the curing process. Specifically, gel time can be defined as the duration required for the degree of cure to surpass 50% across the thickness of the composite part [64], [68]. At this point, the resin permanently changes into a solid state that cannot be processed, shaped, or reformed [69]. The resin’s viscosity quickly increases during gelation, making it more challenging to remove any trapped air [84]. Therefore, controlling the gel time is essential to ensure that the composite part is free from defects such as voids and porosity [85]. This means that the gel time has its effects on the final mechanical properties of the composite, as a well-controlled gelation process can lead to a more uniform and defect-free material. 17 Figure 3. Schematic of thermoset resin curing stages: starting from (A)uncured monomers, moving to (B)increasingly viscous material before gelation, then to (C)gelled but not fully crosslinked network, and finally to (D)fully cured thermoset resin [69] 2.4 Machine Learning in Industrial Applications The implementation of machine learning (ML) into industrial applications has helped multiple sectors by increasing effectiveness, accuracy, and forecasting ability. This technology is highly useful for a variety of industrial applications due to its potential reliability and adaptability [86], [87]. This section explores several real-world instances of machine learning being used in a variety of industries to anticipate material characteristics, machine vision capabilities and predict process outcomes, especially with regard to autoclave curing operations. 2.4.1 ML Applications to Predict Properties Machine learning has found extensive applications in predicting the properties of materials, which is crucial for ensuring quality and performance in various industrial processes. For instance, the use of plastic bottles that are mostly composed of polyethylene terephthalate (PET) in reinforced concrete was investigated in a study by G. Ramkumar et al. They evaluated PET fibre-reinforced concrete (PFRC) to show its potential advantages using a machine learning technique. With a correlation coefficient (R) of 98%, their model predicted PFRC attributes with high accuracy. They were able to demonstrate, via the use of machine learning, the substantial influence 18 that PET fibre’s quantity has on compressive strength, with the study also identifying that the shape of PET fibre affects the material's performance [88]. Similarly, Nakkeeran et al. [89] focused on optimizing construction supplies by understanding the mechanical characteristics of glass fibre mortars. To predict these attributes, they used machine learning techniques, including Artificial Neural Networks (ANN) and Response Surface Methodology (RSM). In the said study, various quantities of glass fibre, hydrated lime, and fly ash were used to substitute 20% cement in the mortar. While both RSM and ANN models predict mechanical attributes with a high degree of accuracy, ANN models performed better [89]. The examples provided highlight how machine learning can accurately anticipate material characteristics, enabling manufacturers to produce higher quality and more reliable substances for construction. 2.4.2 A Synopsis of Machine Vision Applications A form of machine learning named machine vision has been widely applied to improve automation, quality control, and inspection procedures across a variety of sectors. Machine vision is a subset of ML that uses image processing and computational algorithms to perform required tasks [90], [91]. By way of example, Ropelewska & Rutkowski aimed to create a fast and accurate approach to identify the peach ripening phases using image texture metrics and conventional machine learning techniques. Through the examination of slice photos and the application of machine learning algorithms, they created models that categorize ripening phases according to textural characteristics. Up to 100% accuracy in identifying ripening phases was achieved, and notable variations in fruit attributes were noted throughout classes. This approach helped to choose peaches at the appropriate maturity stage for consumption and processing, consequently increasing perceived value and consumer preference [92]. In the medical field, enhancing the distinction of cirrhosis stage diagnosis in Magnetic Resonance Imaging (MRI) scans is vital for accurate medical assessments. K. Prakash and S. Saradha proposed a method that integrates texture discovery from MRI images with classification techniques. The process uses preprocessing MRI images to figure out Region of Interest (ROI) fields. After that, Local Binary Pattern (LBP) filtering is applied to those ROIs then from there followed by extracting Haralick texture features from the Gray-Level Co-occurrence Matrix (GLCM). Figure 4 shows a liver as a sample, and ROI was used to extract features. Support Vector Machine (SVM) classification was then used to predict cirrhosis disease. The study depicted the 19 effectiveness of this method in accurately diagnosing hepatic cirrhosis, showcasing the potential of machine vision in enhancing medical diagnostics [93]. Figure 4. A liver sample with the found Region of Interest (ROI) to extract textural features [93] Another case use is for efficient control of heating, ventilation, and air-conditioning (HVAC) systems in buildings which is crucial for energy efficiency and comfort. Yaa Takyiwaa Acquaah et al. explore feature extraction techniques, including Haralick texture features and wavelet statistics, to detect the number of people in an area using thermal camera imagery. Their results showed high accuracies in detecting the areas with a higher density of people. Multiple models were used in this research, and promising results were obtained from all of them. These models can be integrated into HVAC systems for energy efficiency and security, optimizing resource distribution in populated areas [94]. 2.4.3 ML-Based Prediction of Process Outcomes in Autoclave Processing There have been a few past studies that use machine learning to predict process outcomes in autoclave curing. For example, Yuan Lin and Zhidong Guan investigated five machine-learning models to predict the uniformity in the curing degree of a composite autoclave. They examined a fully connected neural network (FCNN) model, a deep neural network (DNN) model, a radial basis function (RBF) neural network model, a support vector regression (SVR) model, and a K-nearest neighbours (KNN) model. The input parameters for these models were the heating rate, holding time, and holding temperature of the composite material’s two holding-stage cure profiles. The goal was to create a quick estimation model of the maximum curing degree difference during 20 the curing process. The result demonstrated the potential and accuracy of these models to predict the targeted process outcome [78]. Furthermore, Milad Ramezankhani et al. [67] proposed a transfer learning framework that adapts to shifts in autoclave curing production design while being needless of retraining and preventing data loss in the presence of severely limited data. The proposed method used sequential unfreezing and early stopping methods to handle production uncertainties effectively. The proposed model’s output was the history of the thermal process, whether thermal lag or exotherm. Exotherm is the peak temperature reached by the part during curing, while thermal lag is the difference between the part’s temperature and the prescribed temperature settings of the curing cycle. In an industry-focused case study on autoclave composite processing, the TL framework demonstrated an 88% improvement in generalization accuracy compared to conventional learning while reducing computational and temporal costs by 56% [67]. To summarize, different methods and subsets of machine learning have proven to be effective in various industrial applications, to control the quality of the final outputs. The use of machine learning in production not only has enhanced efficiency and accuracy but also encourages innovation and quality in industrial processes. Therefore, it is only logical to use these tools in areas where there is still a gap for more innovative and nearly real-time quality control and process optimization tools. 2.5 Summary This chapter reviewed the critical aspects of composite autoclave curing processing, challenges in the process design, and the underlying physics of heat transfer. The discussions highlighted the complexity of achieving the curing outcomes due to the intricate interaction of temperature, pressure, and material properties. These variables, which could vary with each curing condition, lead to different outcomes (and hence part qualities), making it crucial to optimize them. Additionally, controlling all these variables is not always feasible in practice. Conventional, fully simulation-based, or on-site trial and error methods to optimize the process may not be cost and time-efficient for all industries. However, using machine learning prediction models as a fast prediction and optimization tool, especially when integrated with the application of Haralick texture features that relate to the thermal images often collected in the composite processes, would represent a promising path to bridge this gap. The latter motivation has been the base of the proposed method in this research, as explained in the next chapters. 21 Chapter 3: Methodology In this chapter, a general description of the proposed methodology and its application/case study is described first. Next, three different practical scenarios are introduced (each addressing one of the sub-objectives defined in section 1.2), based on synthetic data generated from the case study. After that, the chapter provides a detailed explanation of the AI-based methodology. It starts with how the collected (simulated) thermal camera image or video is processed using a time-series heatmap. Then, the feature extraction process is explained, which generates inputs for the machine learning (ML) models. Finally, the chapter covers the specific ML technique chosen to predict the process outcomes discussed in Chapter 2 (section 2.3), as well as the means to evaluate and control the fitting of the model. 3.1 Case Study This simulation-based case study was selected to mimic a real-world factory operation scenario where most if not all, composite samples during autoclave trials should be fully/nearly fully cured. By predicting the cure outcomes via an AI model, designers can adjust the next process to achieve more desirable results. 3.1.1 Material and Process Descriptors Selected The case study assumed the following design inputs and their constraints: • Composite Material: HEXCEL 8552-1/IM7 (fixed); • Tooling Material: Invar 36 (fixed); • Thickness of the Composite (Part): 25mm (fixed); • Thickness of the Tooling: 25mm (fixed); • Top HTC (𝑊𝑚2𝐾) which can vary: 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150; • Bottom HTC (𝑊𝑚2𝐾) which can vary: 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150; • Cure Cycle: Manufacturer’s recommended cure cycle (MRCC) should be used – Slow. (fixed) As apparent from the above description, most controllable factors from a designer’s perspective, or as related to the use case of the cured part, are kept constant. The only 22 decision/optimization variables are assumed here to be the top-side HTC and bottom-side HTC for two main reasons. First, the exact value of HTC often cannot be fully controlled in practice and can vary during the curing process at every location of the autoclave [63], [64]. Second, HTC may significantly impact the outcomes of the curing process, often leading to defective samples if not chosen appropriately [63]. As a result, in this case study, the part design parameters such as thickness and material are held constant, while HTC is treated as the primary variable influencing the curing process. The chosen range for HTC was from 10-150 𝑊𝑚2𝐾 to represent the majority of the possible HTC range within an autoclave curing process in practice [64], [67]. Therefore, HTC serves as a critical design variable, and thereby a full factorial sample creation was used. Namely, considering the possible combinations of the top and bottom HTCs, the total number of samples in this design was 152 (or 225). The primary process quality outcomes/outputs were chosen to be: • The Minimum Degree of Cure: The lowest value of DOC through thickness after the curing process. • Uniformity Factor of DOC: Calculated using the formula described in Chapter 2 (eq. 4) • Gel Time: The time that it takes for the degree of cure to surpass 50% across the thickness of the composite part [64], [68] • Cure Time: The duration required for all the part thickness to reach the predetermined DOC. For the purpose of this study, 0.86 was set to be considered the minimum required DOC for a part to be considered fully cured. Typically, the composite parts are made of prepregs during autoclave processing, and they often come with a nominal cure cycle recommended by the resin manufacturer, which the production designers can then use as a first guideline to construct/optimize their own cure cycle, given specific tooling material part geometry etc [95]. The prepreg chosen for this study was HEXCEL 8552-1/IM7, made by Hexcel Corp. This prepreg contains HexPly 8552, a high-performance tough epoxy matrix commonly used in primary aerospace structures. It provides high impact resistance and damage tolerance across various aerospace applications [96]. The fibre component is HexTow IM7, a high-performance continuous carbon fibre with intermediate modulus. The fibre has undergone surface treatment and sizing to 23 enhance its interlaminar shear properties, handling, and structural characteristics. The distinctive features of HexTow IM7, including high tensile strength and modulus, along with good shear strength, can enable structural designers to have higher safety margins for applications where both stiffness and strength are critical [97]. Additionally, in the present case study, Invar 36, selected as the tooling material to support the part, is a nickel-iron alloy containing 36% nickel. It maintains nearly constant dimensions over the range of normal atmospheric temperatures and has a low coefficient of expansion from extremely low temperatures up to approximately 260°C [98], [99]. Given the above prepreg and other fixed process parameters (summarized in Table 1), the cure cycle chosen to cure the part was a version of the recommended MRCCs of the RAVEN simulation tool and Hexcel Corp, with a slower range of heating rate (Figure 5). This ensured that most parts (samples) could be fully cured without unwanted defects such as high-temperature overshoot. Table 1. Summary of Case Study Process Parameters Fixed Composite Tool HTC Thickness No. of samples HEXCEL 8552-1/IM7 Invar 36 10 to 150 𝑊𝑚2𝐾 25 mm (Part) 25 mm (Tool) 225 Figure 5. Visual Description of The Case Study and the Chosen Cure Cycle. 3.1.2 Simulation in RAVEN for data generation The samples (dataset) needed for this study were created using cure simulation in the RAVAN software --Version 3.15.3, developed by Convergent Manufacturing Technologies Inc., [100]. Simulations are valuable in practice for several reasons. They can help lower costs and save 24 time, especially when there are limits on the number of real-life samples tested during the design process. This is particularly useful during proof-of-concept trials. Similarly, in the present feasibility case study, changing HTCs for each trial via actual experiments would be very costly and time-consuming, while the RAVEN software has been already validated against real experimental data in past studies, [100]. 3.1.2.1 Simulations Assumption and Accuracy RAVEN is a user-friendly software designed for the simulation of composite processing, especially in autoclave manufacturing. It can evaluate the temperature and material property evolution of composite laminates. Supporting both thermoset and thermoplastic matrix materials, it has been proven to be suitable for most polymer matrix composite processes once the fibre and matrix have been combined into a composite. RAVEN can fully simulate the curing process with materials from its existing library and calculate properties such as DOC, ply, and resin modulus [100]. This demonstrates the software’s potential to accurately predict the process outcomes of cured composites. Delving deeper into RAVEN simulation software, it is important to note that the version used, could only provide the thermal history of a part in 2D simulations, not the process outcomes. Therefore, for this study, a 1D parts for simulation were assumed to ensure that the data samples had corresponding outputs needed for AI training. Moreover, in composite curing, heat typically moves from the heat source through the thickness of the composite material. Because the in-plane dimensions are much larger than the thickness, the conductive heat transfer inside the part tends to be more efficient in the thickness direction due to the shorter distance and the effect of temperature difference [101]. Therefore, assuming large parts, the in-plane temperature gradients may be negligible, allowing a 1D approximation of heat transfer. Furthermore, spatial variability in HTC would have a negligible effect on the in-plane temperature gradient compared to the transverse gradient [64]. This assumption is not uncommon, as there are studies, such as Fisher et al. [64], which simplified their simulation process and arrived at a close approximation of any specific area in a 2D part. To demonstrate the accuracy of RAVEN process simulations, Molly Hall et al. [102] conducted a study comparing RAVEN-predicted curing parameters with experimental results obtained through dielectric analysis. Dielectric analysis (DEA) is a technique that measures the electrical properties of a material during curing, such as ionic conductivity, impedance, and 25 permittivity, in order to monitor physical changes like the degree of cure (DOC). The method is particularly effective because these electrical properties correlate directly with the curing process, providing insights into critical stages such as gelation and vitrification. Figure 6 (a) shows the degree of cure (DOC) calculated from experimental dielectric analysis compared to DOC predictions from RAVEN for one of their test replicates. Results in Figure 6 (b) further validated this by comparing RAVEN simulations with experimental degree of cure predictions derived from multiple dielectric parameters, including impedance (resistance to electrical flow), ionic conductivity (movement of charged particles), and loss factor (energy dissipation in the material) [102]. These findings highlight the capability of RAVEN to predict curing outcomes with agreeable precisions. Figure 6. (a) DOC comparison between RAVEN simulations and dielectric analysis (b) Correlation of RAVEN-simulated DOC with experimental results from multiple dielectric parameters [102]. In another study, Hudson, T., & Yuan, F. performed a study aiming to evaluate the cure of epoxy-reinforced carbon fibres using guided-wave ultrasonic monitoring. They used the results from RAVEN simulations to validate their experimental results. Their experimental results, such as minimum viscosity and gelation, showed a good correlation with the outcomes of RAVEN [18], [103]. There are also studies that use the results of other simulations and verify them using the outputs from RAVEN [104]. Additionally, prior studies have researched and validated the results of RAVEN’s simulation for 1D composite curing processes in the literature [105]. This showcases the potential of this software to help industries such as aerospace and automation evaluate the quality and performance of their products while reducing costs and maintaining safety [106]. 26 Hence, using RAVEN for sample generation in simulations was deemed the most appropriate choice for this study, ensuring high accuracy with minimal cost and time limitations. 3.1.2.2 Synthetic Data Collected The following data were collected from each sample (simulation): • Temperature of each material element through time (temperature gradient of the thickness through time). • Degree of Cure (DOC) values of each presented element through time. This helps also to pinpoint any other DOC-related value to extract, such as gel time and cure time. • The time steps that correspond to all these values. All these values are crucial for the post-processing step to construct the ANN models' inputs and outputs. In Chapter 4, the exact method and steps used to benefit from this information will be explained in more detail. 3.2 Defining Application Scenarios As discussed earlier (Section 1.2), three examples (potential application scenarios in practice) were selected to test the AI method of this research. Consequently, the ANN models will be tested in each example. 3.2.1 Example 1 (Prediction Mode): Ideal Condition with No Uncertainty in Temperature Measurements As mentioned previously, all of the data collected from the simulation is time-based. These time steps are very close together and are determined by increments in the simulation. They are so close (timestep of ~30 sec) compared to overall cycle time (~400 min) that they can essentially be considered continuous. The ideal condition assumes the designers can capture all these values in real life using a thermal camera that corresponds to time steps very close to one another, and with no error. With all the corresponding numbers, the next step, which is preprocessing, can begin. Therefore, in this scenario, the maximum amount of information possible about the curing process is collected. Proving the study’s concept under this scenario shows the potential of the concept working under ideal conditions where the camera or other means of measuring temperature continuously operate without interruption. 27 3.2.2 Example 2 (Prediction Mode): Mimicking Real-Life Implementation with Uncertainty in Temperature Measurements Capturing the temperature of the part throughout the curing process using simulation is easy and poses no extra challenge. However, since this study serves as a feasibility study for future real-life implementation, after proving the concept under ideal conditions, the focus must shift to actual execution. Capturing all the temperature values continuously can be challenging in practice, and there would also be errors in the measurements at each timeframe. Thus, the concept had to be tested in a way that depended on fewer temperature measurement instances. In Section 4.2.2.1, these possible errors in temperature measurements in the thermal camera will be explained in more details, including how measuring fewer temperatures helps mitigate this effect during modelling. Also under this scenario, a sensitivity analysis will be performed on the ANN models to evaluate their response to errors. In sensitivity analysis, the effect of uncertainties in the models’ inputs on the accuracy of the outputs is investigated. Performing a sensitivity analysis in design studies is key as it helps assess the model’s robustness in response to uncertainties added to elements prone to it [107], [108]. 3.2.3 Example 3 (Reverse Engineering Mode): Multi-Objective Design Optimization The purpose of this scenario is rather different from the other two. The previous two focused on using process inputs, after processing the temperatures, to predict the process outcomes such as DOC and gel time. Knowing these outcomes allows the designer to manually evaluate and make decisions on how to further optimize their values. There may be also conflicting outcomes where enhancing one outcome makes another less desirable (this will be shown further through results in Chapter 4). Hence, there can be a trade-off that should be resolved during decision making. The goal of the third scenario is to help the designer further in such cases and make this decision process more automated via a reverse engineering (optimization) routine under, conflicting process outcomes. As previously mentioned in Section 2.2.4, manufacturers are often unable to set the HTC values to an exact designed number, and without sensors at every location, their value is unknown [63], [64]. Yet, knowing/estimating the current HTC range via a data-driven model can enable the designer to push the HTC values closer to their optimal range. To address this notion, the third scenario first tries to directly use the measured temperature values and predict process outcomes (similar to the previous two examples), without using the HTC values as input of a data-driven 28 model. Then, a second ANN model will be developed to predict the HTCs in a reverse engineering manner, where this time the inputs are used to predict manufacturing conditions (HTC values). Finally, an optimization model (based on Genetic algorithms) will be established to estimate the desired HTC range given the optimal design requirements [109]. Genetic algorithms (GA) [109] are known optimization techniques inspired by the process of natural selection and principles of genetics, which are used to generate high-quality solutions to optimization and search problems [110], [111]. Using a genetic algorithm, the presented samples will be optimized under the given optimization conditions. The genetic algorithm adjusts the inputs of the real-life scenario and predicts its outcomes. If the outcomes are close to objectives and within constraints, they are kept, and it keeps regenerating the samples [112]. After identifying the Pareto front solutions with GA, the optimal solution and its corresponding HTC values, or a possible range, can be determined using decision-making models like TOPSIS [113]. Then, based on the current state of the manufacturing process, the designer can make further adjustments to optimize the process outcomes. The details of this part of the methodology will be further explained in Chapter 4 (section 4.3.2) 3.3 Time-series Heatmap The data collected from the simulations for each scenario needed further processing to prepare them for developing the ANN models. As mentioned in Section 3.1.2.2, the temperature values of the 1D thickness were collected over time. To mimic what a thermal camera would capture when scanning the thickness over a time period, these temperature values need to be constructed into an image. To explain further, if the data were collected using an actual camera, each time step (or frame in a video) would represent an image of the thickness of the curing part with its temperature gradient. Therefore, each frame represents the temperature distribution of the part’s thickness. When these frames are put next to one another, they construct a time-series heatmap of the entire process. This image stores the information of temperature changes through time in that cross-section, where the colour of each pixel represents the temperature at that specific location at a specific time. Meaning that essentially this is converting all the temperature values into an image to represent each sample’s curing history. 29 3.3.1 Global Grey Scale for Temperature Distribution A custom scaling system must be developed to convert all the temperature values to their corresponding colour. This means that in that scale, any range of temperatures possible for the process corresponds to a specific colour. This scale is custom and based on the approach in each scenario. This will be further explained in the next chapter, and the scale for each scenario will be presented, along with the reasoning behind choosing it. Since the goal is to use the final image to extract Haralick features from it, the final image should be in shades of gray [114]. This means that in the custom scale, the highest temperature is equivalent to black and the lowest is white, with anything in between being the possible shades of gray between black and white. This grayscale should be global in each scenario, meaning that if a specific scale is used to construct images for training a model, that scale should be used for any samples used for validation or testing on the same model. A custom grayscale is presented here to showcase the points explained in this section. This scale was not used in any of the scenarios and only serves as an example. If, for this custom grayscale, the top temperature is 100 ℃ and the lowest is 20 ℃, and the steps of dividing them into small ranges are 4 ℃, the final scale is shown in Figure 7. This scale has 20 (100−204) shades of gray with the lowest equal to white and the highest to black. Figure 7. Custom Grey Scale for Temperatures 3.3.2 Combining Camera Frames After setting the grayscale for each scenario, at any given time (or frame in the video), there will be a 1D cross-sectional area with temperature distribution on it according to that scale. As mentioned previously, these 1D frames can be put next to one another to construct a 2D image, showing the history of cure over time. The vertical direction on the image represents the thickness, with the lowest point being the bottom of the part getting cured and the highest point being the top side of the composite. Since the frames are put together, the horizontal direction represents the time, starting from left to right. For example, the bottom left corner of the time-series heatmap 30 image represents the temperature of the bottom point of the composite part at the start of the curing process. It is worth mentioning that the time steps are different in the ideal condition scenario and mimicking real-life implementation for the reasons mentioned previously and more that will be explained in the next chapter. However, the concept for both remains the same. To depict an example of this concept, Figure 8 shows one sample from the ideal condition scenario where the top-side HTC is 90 𝑊𝑚2𝐾 and bottom-side HTC is 120 𝑊𝑚2𝐾. This image has its own grayscale that helped convert the temperature values to corresponding colors, which was not the same as the custom scale above. Therefore, the corresponding scale is provided on the side of the figure. This scale will be explained in the section 4.1 in the next chapter. The time steps in this scenario were very close (decided by the simulation increments), so the image presented here is very continuous, and the temperature gradient of the curing process is easily seen. Figure 8. An Example of the Time-series Heatmap with its Scale on the Side 3.4 Haralick Features and Their Extraction After converting all the temperatures to an image that represents each sample, the next step is to extract the texture features of that image. Feature extraction is necessary for this approach as the goal is to use these images as the inputs for the ANN models and predict autoclave process outcomes. Using image texture feature extraction essentially converts the image into a set of condensed numbers (metrics) that represent the image’s main information [115]. The method used here to extract these features was based on Haralick features. Haralick features were first 31 introduced by Haralick in his 1973 [114], as Textural Features for Image Classification. This method has evolved since then and has been used in many types of research with different goals. For instance, in additive manufacturing, accurately predicting product properties in laser powder bed fusion (LPBF) for metal additive manufacturing which can be useful in various industries, like manufacturing, and aerospace. Lien-Kai Chang et al. developed a method using machine learning to predict these properties. They extracted image textural features from layer-by-layer images using the Haralick texture features and employed several models for prediction. Results showed strong correlations between process parameters and texture features proving the potential. Then, the models demonstrated high predictive accuracy, with R-squared scores approaching 0.9 for all properties, highlighting the feasibility and superiority of the proposed approach in meeting diverse application demands [116]. In essence, the Haralick features are a set of statistical measures that capture the textural properties of an image. They are derived from the Gray Level Co-occurrence Matrix (GLCM) and provide information about the spatial distribution of pixel intensities [114], [117]. Haralick features include measures such as contrast, correlation, energy, and homogeneity. These features are essential for image analysis as they summarize complex textural patterns in a concise and meaningful way [118], [119]. They can then be used for various image-processing tasks, like the approach chosen in this study. In addition to Haralick features, other image feature extraction methods, such as Convolutional Neural Networks (CNNs), have been widely used for tasks like image classification, detection, and regression. CNNs automatically learn features from raw image data through convolutional layers, eliminating the need for manual feature engineering. These architectures, such as AlexNet and VGG, have achieved impressive performance in various image processing tasks. However, CNNs are often regarded as black-box models due to their complex multi-layered structure, which can obscure how predictions are made. This lack of interpretability presents challenges in industrial applications where understanding the model's decision-making process is critical. Furthermore, CNNs typically require large datasets and substantial computational resources to train effectively, making them less feasible when resources are limited [120], [121]. In contrast, Haralick features, which capture the spatial relationships between pixel intensities, offer a more interpretable approach to texture analysis. These features are computationally efficient and transparent, making them suitable for applications such this project, 32 where dataset size and computational resources may be constrained. Although CNNs are powerful tools for automatic feature extraction, Haralick features provide more simplicity, efficiency, and interpretability that aligns with the objectives of this research [122]. In this case, the use of Haralick features seems to strike the right balance, capturing valuable texture information without the complications that come with black-box models, like CNNs. The GLCM is used to calculate Haralick features. The GLCM is a matrix that tabulates how often pairs of pixels with specific values (gray levels) occur in an image in a specified spatial relationship. Essentially, it is a way of examining the spatial relationship of pixels. Each element (i, j) in the GLCM represents the number of times the pixel with value i is adjacent to the pixel with value j. The GLCM is created by comparing a pixel with its neighbours and can be computed in various directions and distances, which helps capture different textural details [123], [124]. Calculating the GLCM involves scanning the image and determining how frequently pairs of pixel values occur in specified spatial relationships (e.g., horizontally, vertically, or diagonally adjacent). Once the GLCM is constructed, various statistical measures, known as Haralick features, are computed from this matrix [114]. By capturing these textural properties, Haralick features provide a robust way to characterize an image's texture, which can then be used as input for predictive modelling. Recalling Figure 9, the construction of the GLCM can be explained more clearly. The first step is to choose a direction. In this example, the horizontal direction to the right, or 0˚, is selected. The GLCM calculates all the combinations of pixel intensities that occur in this direction. Specifically, for any pixel with an intensity of i, it determines how often a pixel with an intensity of j appears to its right. Therefore, at (i, j) in the GLCM, the count of this combination is recorded. For instance, if i is chosen to be 3 and j is 2, the GLCM will count the number of times a pixel with intensity 2 is to the right of a pixel with intensity 3. In the given figure, this happens twice. However, a pixel with intensity 2 also appears once to the left of a pixel with intensity 3, but since the direction is set to 0˚, this occurrence is not counted. The same process is applied to other combinations, such as i=3 and j=1, to create the base GLCM. The final step is to normalize the numbers in the GLCM by dividing each value by the sum of all the values. This normalization results in a normalized GLCM, where p(i,j) represents the probability of the combination occurring. These normalized values are then used to calculate the targeted Haralick features [125]. 33 In the following sections, the features that were used in this study are presented with their definitions and the way they are calculated. Figure 9. An Example of how GLCM is constructed [125] 3.4.1 Contrast Contrast measures the intensity of contrast between a pixel and its neighbours over the entire image. It provides information about the level of local variations present in the image. High contrast values indicate significant intensity differences between pixels next to one another, which usually comes with sharp edges and high-frequency textures in the image [117], [118]. Mathematically, contrast is calculated as: 𝐶𝑜𝑛𝑡𝑟𝑎𝑠𝑡 = ∑ (𝑖 − 𝑗)2 ∙ 𝑝(𝑖, 𝑗)𝑖,𝑗 (5) where: - 𝑝(𝑖, 𝑗) is the normalized GLCM value at (𝑖, 𝑗) 3.4.2 Correlation Correlation measures how correlated a pixel is to its neighbour over the entire image. It tries to show the linear dependency of gray levels on their neighbouring pixels. Correlation is a way to find the degree of correspondence between a pixel and its neighbouring pixels. High correlation values indicate that the gray levels are highly dependent on each other, which usually suggests a uniform texture [117], [118]. Correlation is given by: 34 𝐶𝑜𝑟𝑟𝑒𝑙𝑎𝑡𝑖𝑜𝑛 = ∑ ∑ 𝑝(𝑖, 𝑗)(𝑖 − 𝜇𝑥)(𝑗 − 𝜇𝑦)𝜎𝑥𝜎𝑦𝑗𝑖 (6) where: 𝜇𝑥 = ∑ ∑ 𝑖 ∙ 𝑝(𝑖, 𝑗)𝑗𝑖 | 𝜇𝑦 = ∑ ∑ 𝑗 ∙ 𝑝(𝑖, 𝑗)𝑗𝑖 𝜎𝑥 = √∑ ∑ (𝑖 − 𝜇𝑥)2 ∙ 𝑝(𝑖, 𝑗)𝑗𝑖 | 𝜎𝑦 = √∑ ∑ (𝑗 − 𝜇𝑦)2 ∙ 𝑝(𝑖, 𝑗)𝑗𝑖 (7) 3.4.3 Angular Second Moment (ASM) ASM is a measure that assesses the local uniformity of the gray levels. It’s another way of quantifying the uniformity of the image differently. When the gray levels in the pixels are the same and repeated multiple times, the ASM value will be high. ASM can be calculated as shown in the formula below [117]: 𝐴𝑆𝑀 = ∑ ∑ (𝑝(𝑖, 𝑗))2𝑗𝑖 (8) 3.4.4 Energy Energy is a direct derivative of ASM. Therefore, it also assesses the uniformity of gray levels within a local area. A higher Energy value indicates more remarkable similarity among pixels, reflecting increased uniformity. Energy is calculated as below [117]: 𝐸𝑛𝑒𝑟𝑔𝑦 = √𝐴𝑆𝑀 (9) 3.4.5 Homogeneity Homogeneity measures the closeness of the distribution of elements in the GLCM to the GLCM diagonal [126]. It assesses how similar the gray levels of pixels are to their neighbours. To put it simply, homogeneity calculates how evenly distributed an image's texture is by investigating how frequently similar pixel values occur next to each other. If the values in the GLCM are clustered around the diagonal, it means the image is more homogenous [127]. Homogeneity is determined as below [117]: 𝐻𝑜𝑚𝑜𝑔𝑒𝑛𝑒𝑖𝑡𝑦 = ∑ ∑11 + (𝑖 − 𝑗)2𝑝(𝑖, 𝑗)𝑗𝑖 (10) 35 3.4.6 Dissimilarity Dissimilarity is similar to contrast but gives a linear weight to pixel pairs instead of a squared weight. It measures the variation of gray level pairs in the image, where higher values indicate higher variation. Dissimilarity is calculated as [128]: 𝐷𝑖𝑠𝑠𝑖𝑚𝑖𝑙𝑎𝑟𝑖𝑡𝑦 = ∑ |𝑖 − 𝑗| ∙ 𝑝(𝑖, 𝑗)𝑖,𝑗 (11) 3.4.7 Haralick Features vs. Process Outcomes: Hypotheses Haralick features adopted in this research, assess specific characteristics within the texture of a thermal image [115]. The advantage of using these features would lie in their potential to provide an added layer of interpretability for designers with respect to the process outcomes of the autoclave curing process. By analyzing the trends between Haralick features extracted from time-series heatmaps and the corresponding process outcomes, meaningful correlations are expected. For instance, ASM (Angular Second Moment) and Energy, here both measuring the uniformity of a thermal image, are hypothesized to inversely correlate with the process outcome of the uniformity factor for the degree of cure (DOC). The uniformity factor, defined as the coefficient of variation, decreases when the composite curing process is more uniform. Conversely, an increase in ASM and Energy would indicate a more uniform texture in the heatmap, aligning with their definitions. This expcted inverse relationship highlights the interpretability of these features in predicting uniformity-related outcomes. Similarly, Contrast, which quantifies the intensity variations between pixels, would reflect the dynamics of the curing process. A higher contrast would yield more rapid temperature changes in the curing profile. In turn, this could indicate a more efficient curing process, leading to a higher minimum DOC, shorter gel time, and reduced overall cure time. The above hypothesized/expected trends will be further assessed in the Results sections (Chapter 4). If substantiated, this added interpretability feature not only enhances the reliability of the proposed AI-based process optimization methodology, but also provides a deeper understanding of its predictions. 3.5 Machine Learning (ML) Techniques The last step in predicting autoclave process outcomes via the thermal camera measurements involved machine learning (ML) techniques. At this stage, all the temperature data, 36 after being converted into a time series heatmap per Section 3.3, is represented by a set of Haralick texture features (section 3.4). Using the latter feature numbers as the inputs and the process outcomes as the outputs, a data-driven model can be developed. This section presents the methodologies employed to ensure an accurate and robust model for the present application. 3.5.1 Recap: General Context of ML Machine learning (ML) is one of the branches of artificial intelligence (AI) that focuses on developing algorithms that give the ability to computers to learn from data and make predictions or decisions without the need to be programmed explicitly for that certain task [129]. The primary goal of ML is to create models that generalize well from presented data to make accurate predictions when they are given unseen data. There are multiple types of learning, and the most popular ones are known as supervised, unsupervised and reinforcement learning [130], [131]. Reinforcement Learning is a type of model that makes a sequence of decisions by rewarding desirable behaviours and punishing undesirable ones. The model learns to maximize cumulative rewards. This type of learning method is commonly used in robotics, gaming, and navigation [132], [133]. Unsupervised Learning is a type of training a model on data without labelled responses. The model tries to learn the hidden possible relation between the data, such as creating a group of similar data points together. Clustering algorithms like K-means and hierarchical clustering are examples of unsupervised learning [132], [134]. Supervised Learning involves training a model on a labelled dataset, where the input-output pairs are known. The model learns to map inputs to outputs and is evaluated based on its ability to predict the output for new inputs. Examples include linear regression, support vector machines, and neural networks. So, this type of learning can be used for classification problems as well as regression problems, which is the same type as this research. Therefore, supervised learning is the approach that was chosen to perform the regression task [132], [134]. 3.5.2 Artificial Neural Network (ANN) ANNs, chosen also for the present research, are especially useful for tasks such as regression with high dimensional data (such as continuous images, over time etc). ANNs are made from multiple layers, such as inputs, outputs and hidden layers. These layers are made from neurons where the calculations happen. This type of neural network is feed-forward (also known 37 as feed-forward ANN), meaning that the data flows through the system only in one direction (from input to output) [135]. Known for their flexibility and ability to model complex relationships between inputs and outputs, these models make an ideal choice for tasks involving high-dimensional data. Finding nonlinear relationships between inputs and outputs is another perk of them, which is necessary for capturing complex patterns in data [136]. At the same time, they can be scaled up with more layers to capture more intricate details. On a side note, it is worth mentioning that ANNs are a part of deep learning. Deep learning itself is a subset of ML; in particular, it refers to a branch of learning in which, by using multilayered neural networks, which mimics the human brain [136]. 3.5.2.1 ANN Model Inputs The structure of the model begins with the inputs. The input layer consists of neurons that feed the data necessary for the calculations in the hidden layers [135]. In this study, the inputs were the Haralick texture features extracted from the time series heatmap images described in Section 3.2. These features try to provide a close representation of the image data, capturing important textural information necessary for predicting the process outcomes. 3.5.2.2 Hidden Layers and Activation Functions The ANN’s architecture consists of multiple layers, including hidden layers and an output layer. Hidden layers contain neurons that apply transformations to the input data. These transformations happen in the form of activation functions. These functions introduce non-linearity into the network, enabling it to learn complex patterns [137]. Various activation functions can be used, but the Rectified Linear Unit (ReLU) was chosen in this study. ReLU is defined as 𝑓(𝑥) =max (0, 𝑥), allowing the network to learn efficiently by enabling convergence during training [138]. The shape of the ReLU activation function is shown in Figure 10. 38 Figure 10. ReLU activation function (implemented in this study) 3.5.2.3 Implemented Methods to Prevent Overfitting and Underfitting One of the challenges often faced during ANN predictions is overfitting and underfitting [139]. For instance, overfitting means the model might perform well on the data it is trained on; however, it shows poor performance on an unseen data set. This hints at the issue that the model has not found a clear trend or has gotten trained too much on the presented data, making the poor performance on unseen data possible [140]. To ensure that the model does not overfit or underfit the data, standard techniques were implemented here, as follows: Data Splitting: The dataset was divided into three parts: training, validation, and test sets. This is important for assessing the model’s performance on unseen data, which helps prevent the mentioned problems. The training set is used to train the model, the validation set controls the model’s hyperparameters and monitors its performance during training, and the test set is used to evaluate the model’s final performance. Splitting the data will ensure that the model generalizes well to new, unseen data rather than just memorizing one specific trend seen in the training dataset [141]. Monitoring Loss Curves: During the training process, both the training and validation loss function values were monitored. The loss curves help identify overfitting and underfitting scenarios. If the training loss continues to decrease while the validation loss starts to increase, it indicates that the model is overfitting. Conversely, if both training and validation losses are high, 39 it suggests that the model is underfitting. By closely observing these curves, adjustments can be made to the model to improve its performance [142], [143]. The example of both of these scenarios is provided in Figure 11. Early Stopping: Early stopping is a method where the training process is stopped once the validation loss stops decreasing or starts to increase, indicating potential overfitting. This method helps to avoid over-training the model and ensures that the model parameters are optimal. By monitoring the validation loss, the training is stopped at the point where the model performs best on the validation set. However, in practice it can be set that the model tries in certain number of epochs to make a progress in decreasing the validation loss. Then if not successful the training process is stopped altogether [139]. Weight Saving: To further ensure optimal performance, the best model weights based on the lowest validation loss were saved. Saving the best weights helps in achieving a balance between underfitting and overfitting, as it preserves the model parameters that provide the best validation performance. Since as mentioned the model might still have room to try to make improvements. If no improvement is made then the weights of the epoch that has the lowest validation loss will be restored [139]. This can be better visualized in Figure 11. Figure 11. Visualization of overfitting and underfitting in loss curve graph [144] 3.5.2.4 SHAP (Sensitivity Analysis) to Measure Each Input Contribution to the Resulting Model SHapley Additive exPlanations (SHAP) analysis was used to explain how the contribution of each input factor (Haralick features) to the model’s predictions is. SHAP values are based on 40 cooperative game theory and provide a unified measure of feature importance. They help identify which features have the most significant impact on the model’s output, enhancing the interpretability of the model. By using SHAP values, a better understanding is provided for the relationship between input features and predicted process outcomes, ensuring transparency and interpretability of the prominent features. By understanding feature contributions, it enables identifying important features, potentially guiding future data collection and feature engineering efforts [145], [146]. 3.5.2.5 Means to Evaluate the Model’s Performance To evaluate the ML model’s performance, the following common metrics were used: Mean Absolute Error (MAE): It measures the average absolute difference between predicted and actual values, which gives an indication of the model’s accuracy [147], [148]. The formula of MAE is as below: 𝑀𝐴𝐸 =1𝑛∑ |𝑦𝑖 − ?̂?𝑖|𝑛𝑖=1 (12) where: - 𝑦𝑖 are the actual values. - ?̂?𝑖 are the predicted values. - 𝑛 is the number of observations. Mean Squared Error (MSE): The other measure that was used was MSE, which calculates the average of the squares of the errors. It penalizes larger errors more than MAE, providing insight into the variance of the errors [147], [148]. This study used this metric, as the loss function during the training of the ANN models. MSE is calculated as below: 𝑀𝑆𝐸 =1𝑛∑ (𝑦𝑖 − ?̂?𝑖)2𝑛𝑖=1 (13) R-Squared (R²): Plotting the observed (actual) values vs. the predicted values gives the ability to see the data points compared to the 45˚ line since if all the predictions were correct, all of them would fall on that line. Therefore, to measure the fitness level and the accuracy of the predictions, R-squared (R²) is used. It is a statistical measure representing the proportion of the 41 variance in the dependent variable that is predictable from the independent variables. It provides insight into how well the model’s predictions match the actual data [147]. The formula for R² is: 𝑅2 = 1 −∑ (𝑦𝑖 − ?̂?𝑖)2𝑛𝑖=1∑ (𝑦𝑖 − ?̅?)2𝑛𝑖=1 (14) where: - ?̅? is the mean of the actual values. Looking at the formula, it is apparent that an R² of 1 indicates that the model perfectly predicts the values, meaning that all data points are exactly on the regression line. Therefore, achieving numbers close to that value shows goodness of fit. It’s one of the easiest to interpret and reliable measures to evaluate the model’s performance [147]. Residuals Plot: The plot of predicted values vs. residuals (the differences between predicted and actual values) helps identify any patterns in the errors and see the value of the difference between actual vs predicted values. The residuals can be determined like below: 𝑅𝑒𝑠𝑖𝑑𝑢𝑎𝑙𝑠 = ?̂?𝑖 − 𝑦𝑖 (15) Error Percentage Plot: The plot of actual values vs. the error percentage in predicting these values helps understand the distribution and scale of errors across different value ranges. The error values are calculated as such: 𝐸𝑟𝑟𝑜𝑟% =|𝑦𝑖 − ?̂?𝑖|𝑦𝑖× 100 (16) Following and monitoring these measures gives a comprehensive and proper idea of the model’s performance on unseen datasets. The first three give values to measure the model’s accuracy, while the last two plots and the graph for R² allow visualization of the model's outputs. This shows the potential of R² and its plot of actual vs. predicted values to be comprehensive and thorough. 3.6 Summary This chapter focused on the methodologies used in the study along with the definitions of different application scenarios (examples). The prepreg materials used was HEXCEL 8552-1/IM7 composite, along with an Invar 36 tooling, as well as the design variables that were selected to be the top-side and bottom-side Heat Transfer Coefficients (HTCs). To create the necessary samples 42 (synthetic data), the RAVEN simulation software was used, due to its capability to accurately replicate the autoclave curing process data in 1D design cases, and giving detailed thermal histories. After the sample creation and data collection, the steps in processing the data before feeding it into ML models were explained. Namely, the temperature data collected over time domain in the simulations had to be converted to a grayscale time-series heatmap image containing the temperature gradient of the process on the material thickness. After that, the Haralick texture features of the images were extracted, and the ANN model can be trained on those features. The model’s performance can be evaluated through various metrics such as MAE, MSE, R-Squared, along with the Residuals Plot and Error Percentage Plot. Figure 12 is provided as a visual summary of all the steps perceived for the proposed AI-based approach in autoclave process perdition and optimization. Further elaborations on the steps will be exemplified in the following chapter. 43 Figure 12. Summary of the Methods and Workflow used in this Study 44 Chapter 4: Results and Discussion This chapter presents the results obtained by applying the methodology described in Chapter 3 (and summarized in Figure 12) to the different scenarios/examples (defined in Section 3.2). The outcomes of executing the methodology start with an ideal condition (Example 1) where no uncertainties in thermal camera measurements are assumed. Following this, the results of implementing the same AI-based approach in a more practical approach are presented, as in example 2, where uncertainties in thermal camera measurements are assumed. Finally, as in example 3, the multi-objective (inverse) optimization case results are detailed. 4.1 Example 1: Ideal Condition The ideal condition assumes continuous temperature measurement during the curing process with no errors. The goal in this scenario is to predict process outcomes, such as the Degree of Cure (DOC) and gel time, using only the Haralick texture features extracted from the time-series heatmap image (per Section 3.2.1). To achieve this, the following steps were undertaken: Using all the temperatures captured during the simulation time increments, the time-series heatmap image should be created. As also mentioned in Section 3.3.1, a custom global grey scale needs to be used for temperatures to convert temperature values to the corresponding colour. This greyscale was chosen based on the range of temperatures collected across all the samples. The highest temperature value captured from the selected case study was about 220℃, and the lowest was 20℃. To accommodate potentially higher temperatures, the custom grayscale was defined between 20-250℃ with increments of 3℃, creating 77 (𝑖𝑛𝑡(250−203) + 1) shades of grey. The temperature increment was chosen based on temperature fluctuations and manual adjustments after multiple trials. Having the custom greyscale shown in Figure 13, the time-series heatmap images were created. Each heatmap corresponds to one sample in the case study and represents its thermal history. Figure 14 shows one of the samples that had a top-side HTC of 90 𝑊𝑚2𝐾 and bottom-side HTC of 120 𝑊𝑚2𝐾. Figure 13. The Custom Global Grey Scale used for Ideal Condition Scenario 45 Figure 14. An Example of the Time-series Heatmap Used in an Ideal Condition Scenario The next step was extracting the Haralick texture features from these images. This was done using scikit-image, which is a collection of algorithms for image processing [149]. Using this open-source image processing library, the GLCM was first constructed. The direction chosen for this purpose was horizontal to the right, which is 0˚. This was selected as this direction represents the passage of time within the image, with the neighbouring pixel to the right symbolizing the future state. Using the resulting GLCM, the mentioned Haralick features in Chapter 3 are extracted for each sample, resulting in a set of features representing each sample’s thermal history. 4.1.1 Results The final step involves training the ANN models using the extracted Haralick texture features. The case study provided 225 samples, split into three sets: training, validation, and test datasets, with a typical split ratio of 60%, 20%, and 20%, respectively [150]. This division ensures that the model can be trained effectively while also being evaluated on unseen data to prevent overfitting. The training process was conducted using the scikit-learn library, a widely-used open-source machine learning toolkit in Python [151]. Moreover, all the measures to evaluate and monitor the model’s performance are placed within the structure of the Python code. For this example, the following outcomes were chosen per section 3.1.1: 46 • The Minimum Degree of Cure (+) • Uniformity Factor of DOC (-) • Gel Time (-) • Cure Time (-) The ANN models were trained with all the necessary inputs (the Haralick texture features from thermal camera images and outputs (the above process outcomes). A separate model was developed for each output. This was due to the ability to modify and monitor each model individually in the subsequent examples. Therefore, each output may have its own set of unique plots and model structures. To avoid multi-collinearity (and hence potential overfitting), redundant inputs (features) were removed (among all those described in Section 3.4). This was done by assessing the correlation coefficient between each pair of Haralick texture features. The correlation coefficient between datasets can be calculated using the formula given below [152]: 𝐶𝑜𝑟𝑟. 𝐶𝑜𝑒𝑓 =∑(𝑋𝑖 − ?̅?)(𝑌𝑖 − ?̅?)√∑(𝑋𝑖 − ?̅?)2 ∙ ∑(𝑌𝑖 − ?̅?)2 (17) where: - 𝑋𝑖 and 𝑌𝑖 are individual data points from datasets 𝑋 and 𝑌. - ?̅? and ?̅? are the mean values of datasets 𝑋 and 𝑌. Here, X and Y represent two different features. Suppose a pair of features has a very high correlation coefficient. In that case, it means that they carry redundant information, so choosing just one of them can provide the same contribution to the model’s training. A correlation coefficient of 1, the highest possible value, indicates that the two features exhibit an identical linear trend. Figure 15 shows the correlation matrix between the extracted Haralick texture features in this scenario. The pairwise correlation heatmap shows that contrast and dissimilarity have a correlation coefficient of 1, meaning they are perfectly correlated. Similarly, ASM and energy also exhibit a correlation of 1, which is consistent with their definitions. Additionally, the contrast has a correlation coefficient of -1 with homogeneity, indicating a perfectly inverse linear relationship. Thus, selecting contrast, correlation, and ASM alone captures the essential information the Haralick features provide. 47 Figure 15. Pairwise Correlation Heatmap Between the Haralick Features in Example 1 Table 2 outlines the structure of each output model, including the number of data points in each dataset. The number of hidden layers and neurons was determined using a combination of established guidelines and multiple experimental trials. The number of training epochs was finalized using the early stopping technique, which halts training if the model’s loss function (MSE) does not improve after a certain number of epochs, preventing overfitting. It is important to note that the training process was carried out using normalized data, ensuring that all inputs fed into the model were on the same scale, thereby avoiding issues arising from differences in magnitude across features. Additionally, the MSE and MAE values for each model are reported in the table for the test dataset, ensuring that the reported errors are consistent with the scale of the data. Figure 16 depicts the loss function curve throughout the training process. Each image presents two curves: one representing the loss values of the training dataset and the other representing the validation dataset. While the validation loss does not directly influence the 48 training process, monitoring its curve relative to the training loss curve helps detect overfitting and underfitting. Additionally, the validation dataset’s loss value is used for early stopping and saving the best weights of the trained ANN model, as detailed in Section 3.5.2.3. Figure 17 illustrates the ANN models’ prediction errors after training, while Figure 18 shows the prediction residuals. Table 2. The ANN models’ architecture for each output in an ideal condition scenario (example 1) Process Outcome Hidden Layers and Neurons No. Epochs Data split MSE MAE Min DOC 3 (8 neurons each) 2374 135-45-45 2.35×10-6 8.75×10-4 Uniformity Factor 3 (8 neurons each) 800 2.26×10-7 2.94×10-4 Gel Time 2 (8 neurons each) 1630 11 2.39 Cure Time 2 (8 neurons each) 2205 14.29 2.20 Figure 16. Loss function curves during ANN training for different process outcomes in Example 1: (a) Min DOC, (b) Uniformity factor of DOC through thickness, (c) Gel time, and (d) Cure time 49 Figure 17. Absolute error plots for ANN after training for different process outcomes in Example 1: (a) Min DOC, (b) Uniformity factor of DOC through thickness, (c) Gel time, and (d) Cure time Figure 18. Residual plots for ANN after training for different process outcomes: (a) Min DOC, (b) Uniformity factor of DOC through thickness, (c) Gel time, and (d) Cure time 50 Figure 19 presents a plot of actual values versus predicted values to comprehend better the obtained goodness of fit between the predicted and actual values. This figure visually demonstrates the accuracy of the models and includes the R-squared values for both the entire dataset and the test dataset. These R-squared values illustrate the model’s performance across the data points, providing insight into how well the predictions align with the actual outcomes. Figure 19. R-squared plots for different process outcomes’ ANNs in Example 1: (a) Min DOC, (b) Uniformity factor of DOC through thickness, (c) Gel time, and (d) Cure time 51 Using the SHapley Additive exPlanations (SHAP) values the contributions of each input feature to the model’s predictions are presented. The given bar plot is a direct result of using this library in Python. Therefore, Figure 20 provides a better understanding of the effect of each input in the overall training of the ANN models. Figure 20. Bar plots of SHAP values for different process outcomes’ ANNs in Example 1: (a) Min DOC, (b) Uniformity factor of DOC through thickness, (c) Gel time, and (d) Cure time 4.1.2 Discussion A closer examination of the results from each model in Figures 16 to 19 reveals promising outcomes. Starting with the loss curves presented in Figure 16, the absence of overfitting and underfitting across all models is evident. These curves confirm the effectiveness of techniques such as early stopping and weight saving. Moving to Figure 17, it is clear that the prediction errors are generally very low, with errors below 4% for most predictions, except for the uniformity factor prediction. While the highest error in uniformity factor predictions reaches 50%, the majority of data points (over 90%) have an error of less than 20%. Figure 18 adds further depth to this analysis by showing the offset between actual and predicted values. For instance, the highest residual in predicting cure time or gel time is only 10 minutes, which may be sufficiently accurate depending on manufacturers’ standards. The plots in Figure 19 confirm the high accuracy of all developed models, as demonstrated by the R-squared values, all of which exceed 0.94. The minimal difference between the R-squared values of the whole dataset and the test dataset further supports the absence of overfitting or 52 underfitting. Figure 20 indicates that contrast is a highly informative Haralick feature, playing a critical role in all models by being the top contributing input in three of them. This suggests a strong correlation between contrast and the process outcomes. To support this, Figure 21 illustrates the same trend, showing that each model's most significant contributing factor has the highest correlation coefficient with the corresponding outcome. These findings align with the proposed hypotheses in Section 3.4.7, confirming the expected behavior of the cure process based on the thermal images. For example, a higher contrast value expectedly indicates more significant intensity differences among pixels over time, reflecting more intense temperature changes and, consequently, a better cure state. Figure 21. Correlation heatmap between input features and process outcomes in Example 1 The results derived from applying the presented methodology under this example suggest that, the selected Haralick features contain sufficient information to predict the targeted process outcomes using ANN accurately. This finding demonstrates the potential utility of this method in scenarios where data collection during the curing process is straightforward and free from measurement errors. Consequently, the next logical step is to assess the same methodology 53 under more practical, mimicking real-world conditions. The following chapter will present adjustments to the preprocessing steps necessary to extract features in an industrial setting, further investigating the feasibility of implementing this approach in practice. 4.2 Example 2: Mimicking Real-Life Implementation with Uncertainties in Thermal Measurements Unlike the previous section, which assumed continuous temperature measurement with no error, this example assumes collecting temperature values through the material thickness at fixed time intervals (i.e., discontinuous; incomplete dataset). Additionally, the models’ robustness will be assessed after being trained on accurate measurements, with the presence of errors in the temperature data itself. As such, this scenario itself may be divided into two parts: (1) considering incomplete (discontinuous measurement) data and (2) adding random noise to the latter (incomplete) dataset. Part 1: The process of creating samples without noise but with incomplete data (i.e., part 1 above) for the training phase was similar to Example 1. First, using the temperatures captured at specific time intervals during the curing process, a time-series heatmap image was generated. As before, a custom global grayscale for temperatures was determined to convert temperature values to their corresponding colors. The time interval was selected after multiple trials to identify the minimum number of measurements required to maintain accurate ANN predictions. This interval was determined to be 50 minutes, meaning that only eight separate measurements (400 minutes / 50 minutes) are needed during the process. Furthermore, the custom grayscale was selected based on the temperature range collected across all samples, with larger increments to accommodate the significant temperature changes that occur every 50 minutes. This approach also helps maintain the same pixel colours despite measurement errors since it will be within the range, therefore offsetting potential inaccuracies. The custom grayscale was defined between 20-250℃ with increments of 30℃, creating only eight (𝑖𝑛𝑡(250−2030) + 1) shades of grey. The temperature increments were chosen based on the aforementioned criteria, with manual adjustments made after multiple trials. Figure 22 illustrates the custom grayscale used to build the samples for model training. Each of these heatmaps corresponds to a sample in the case study, representing its thermal history. Figure 23 54 provides an example of a sample with the description of a top-side HTC of 120 𝑊𝑚2𝐾 and bottom-side HTC of 30 𝑊𝑚2𝐾. Figure 22. The custom global grey scale used for Example 2 Figure 23. An example of the time-series heatmap in Example 2 The subsequent steps were identical to those in the previous scenario, utilizing the provided library to extract the Haralick texture features. As before, the horizontal direction from left to right (0°) was used for the GLCM, converting each image sample into a set of image texture features. The only additional difference in this scenario is that the addition of the average of the eight temperature measurements taken at the top and bottom points to the ANN inputs is also calculated and included. Thus, each sample now includes both the Haralick features and the average measured temperatures at the top and bottom. This data augmentation was made with a hypothesis that the top and bottom temperature averages would (a) partly compensate for the lack of complete data under a discontinuous camera operation model (while not adding any operational cost in practice as the camera readings have been readily taken), and (b) enhance the models’ robustness when dealing with noise in measurements. The effectiveness of this addition will be further assessed in the section. 55 4.2.1 Prediction of Process Outcomes The ANN models were trained using Haralick image texture features alongside the average temperature measurements taken at the top and bottom of the part during the curing process. The case study provided 225 samples, divided into three sets: training, validation, and test datasets, maintaining the same split ratio as in the previous scenario. The training process was conducted using the scikit-learn library in Python [151]. In this scenario, the same process outcomes were predicted as before, with the addition of temperature overshoot as a new output. Namely, since continuous temperature measurements were not available in this case, the overshoot value could not be post-carucate, making it a crucial parameter to be part of the model. Note that in Example 1, continuous temperature data captured by the camera would inherently provide the overshoot and it was not needed to be included as a separate output for modeling. The temperature overshoot is defined as follows: • Temperature Overshoot: It is the highest positive difference between the 180°C dwell temperature (in this cure cycle) and the actual temperature of the part through the thickness [64]. Each process outcome was modelled separately, allowing for individual monitoring and adjustments based on performance. The next step was to select the exact inputs to be fed into the ANN models. Using the same method as before, a correlation matrix was constructed between the available inputs. Figure 24 shows that only ASM and energy have a high correlation with each other, which is expected based on their definitions. Consequently, the selected inputs for the models include contrast, correlation, energy, dissimilarity, homogeneity, and the average temperatures at the top and bottom. Figure 26 depicts the prediction errors of the ANN models for each process outcome. Table 3 provides insights into the structure of each model developed for the corresponding process outcomes, including information on the performance of these models using Mean Absolute Error (MAE) and Mean Squared Error (MSE). Figure 25 illustrates the loss function curve during the training process for all models. Figure 27 shows the residuals of predicted values in comparison to the actual value of the outputs. This gives insights into how much offset is between the number that the model forecasts and the actual value of the process outcomes. 56 Figure 24. Pairwise Correlation Heatmap Between Haralick Features in Example 2 (part 1) Table 3. The ANN model’s description for each output in Example 2 (part 1) Process Outcome Hidden Layers and Neurons No. Epochs Data split MSE MAE Min DOC 3 (8 neurons each) 373 135-45-45 1.43×10-6 8.90×10-4 Uniformity Factor 3 (8 neurons each) 3747 2.38×10-7 2×10-4 Gel Time 2 (8 neurons each) 563 3.83 1.35 Cure Time 2 (8 neurons each) 2403 2.13 1.06 Temperature Overshoot 3 (8 neurons each) 5996 1.47 0.67 57 Figure 25. Loss function curves during ANN training for different process outcomes in Example 2 (part 1): (a) Min DOC, (b) Uniformity factor of DOC through thickness, (c) Gel time, (d) Cure time, and (e) Temperature overshoot 58 Figure 26. Absolute error plots for ANN after training for different process outcomes in Example 2 (part 1): (a) Min DOC, (b) Uniformity factor of DOC through thickness, (c) Gel time, (d) Cure time, and (e) Temperature overshoot 59 Figure 27. Residual plots for ANN after training for different process outcomes in Example 2 (part 1): (a) Min DOC, (b) Uniformity factor of DOC through thickness, (c) Gel time, (d) Cure time, and (e) Temperature overshoot Figure 28 presents the R-squared values and plots of the actual vs. predicted values for each output to provide further insights into the models' performance. Finally, Figure 29 provides the SHAP bar plot to determine the contribution of each input to the overall performance of the models. 60 Figure 28. R-squared plots for different process outcomes’ ANNs in Example 2 (part 1): (a) Min DOC, (b) Uniformity factor of DOC through thickness, (c) Gel time, (d) Cure time, and (e) Temperature overshoot 61 Figure 29. Bar plots of SHAP values for different process outcomes’ ANNs in Example 2: (a) Min DOC, (b) Uniformity factor of DOC through thickness, (c) Gel time, (d) Cure time, and (e) Temperature overshoot 4.2.2 Past 2: Adding Noise to the Data and Robustness Assessment As previously mentioned, the goal of this entire example was to apply the proposed methodology under incomplete data (part 1) along with noisy data (part 2) that would more closely mimic real life. The first step (part 1) was to significantly reduce the number of required data collection points, which was successfully achieved through ANN results shown in Section 4.2.1. The next step is to artificially introduce errors into the collected temperature measurements in this example. This provides an opportunity to assess the robustness of the developed ANN models. Since these models were constructed and trained on data provided by simulation, which contains no errors in temperature values, it is necessary to evaluate their performance when applied in 62 practical settings where temperature measurements contain expected errors. This approach, often known as sensitivity analysis, aims to investigate how sensitive the outputs of the models are when facing uncertainties in the inputs [153], [154]. 4.2.2.1 Data Processing for Noise Inclusion While thermal cameras are generally reliable, they can still exhibit errors in capturing exact temperature values. These errors typically range between ±2°C or ±2% of the actual temperature, depending on which is greater [155]. For this study, several adjustments were made to the original pre-processing method to introduce this error into the numerical temperature measurements. After collecting all the (nominal/certain) temperatures from each RAVEN simulation, a new step was added: adding normally distributed error values to the nominal temperatures. At each time interval (eight intervals per sample in this scenario), a single offset value was generated and applied uniformly to the entire temperature profile across the part cross-section. This means that at each specific time step, all temperatures through the thickness of the cross-section were adjusted by the same offset value, simulating the way a thermal camera would capture and measure the entire cross-section as a single frame at once. These offset values were drawn from a normal distribution, ensuring that the introduced variations followed a realistic statistical pattern. To make this happen, a standard deviation of 2°C was chosen for the normally distributed offsets, meaning at least 95.4% of the errors would be between ±4°C, roughly 2% of the maximum measured temperature (around 200°C). This value was selected because it represents the expected error range based on industrial standards for thermal cameras [155], providing a realistic numerical test. It is worth noting that introducing higher standard deviations would likely make the ANN models less robust, as larger measurement errors would amplify uncertainties in the data. However, such scenarios were not considered in this study because the selected error range aligns with precision of commercial thermal cameras, ensuring meaningful performance evaluations. After adding the error to the temperature values, the average temperatures at the top and bottom were calculated, and the time-series heatmap image was constructed. The same grayscale as before (Figure 22) was used to convert temperatures to corresponding colors. Figure 30 shows an example of a time-series heatmap image, with the right side illustrating the image after introducing temperature measurement errors, and the left side is the same image as Figure 23. As 63 apparent in that image, not all the introduced errors lead to noticeable changes in the pixel colours, and the heatmap remains largely intact. This phenomenon can be attributed to two key factors: Grayscale Color Range: Each colour in the grayscale heatmap represents a broad temperature range of 30°C. As a result, even when errors are introduced, many of the affected temperatures remain within the same colour band. This reduces the likelihood of significant visual changes in the heatmap despite the presence of errors. Figure 30. Comparison of the time-series heatmap before and after adding temperature errors in Example 2 Sampling Interval: Identical to Part 1, the thermal camera data collection was assumed to be taken at 50-minute intervals, which means the model relies on fewer (incomplete) measurements for each heatmap generation. The next step was to extract the Haralick features from the constructed time-series images using the same approach and specifications as before. The models were then used to predict the process outcomes using the same inputs as those used for training. It is important to note that the errors described above were only added to the test dataset, as this dataset had no effect on the training process. Furthermore, the test dataset was also evaluated on the models without the added noise, providing a direct comparison to observe the impact of temperature errors on the nominal models’ predictions. 4.2.2.2 Model Predictions After introducing uncertainties into the test dataset inputs, the data was fed into the models trained on error-free data. By comparing these predicted values to the actual simulation outputs, the plots in Figure 31 were created. These plots illustrate the accuracy of the models when faced with uncertainties in the temperature data used to build the inputs. 64 Figure 31. R-squared plots for different process outcomes’ ANN with uncertainties added in Example 2: (a) Min DOC, (b) Uniformity factor of DOC through thickness, (c) Gel time, (d) Cure time, and (e) Temperature overshoot 65 4.2.3 Discussion Starting with the results obtained from training the models on data without added uncertainties (i.e., Figures 25-28), it is evident that even with a reduced number of captures during the curing process, the models are able to learn from the data points and identify trends between the inputs and outputs. For instance, the plots in Figure 26 show that the error percentage for the min DOC, gel time, and cure time is mostly below 2%, demonstrating the accuracy of the predictions. Although the error for the uniformity factor and temperature overshoot peaks at 70%, over 90% of the data points have an error below 15%. Figure 27 further supports this by showing the minimal residuals, with the maximum residual for temperature overshoot being 6℃ and most samples having an offset of less than 2℃—a level that may be considered accurate depending on the manufacturer’s standards. These results were achieved without any signs of underfitting or overfitting, as shown in Figure 25. This is also reflected in Figure 28, where all models demonstrate good fit, with R-squared values above 0.94 across all process outcomes and for both the entire and test datasets. This indicates that the models perform well on the test dataset with minimal deviation from the entire data points. One key difference between these models and those developed in the previous scenario was the inclusion of average temperatures captured at the top and bottom during curing. This information proved beneficial in helping the models learn patterns and enhancing their robustness. Evidence of the former is shown in Figure 29, highlighting the significant contribution of these values in training the models. The latter was observed during the trial-and-error process of model development. While the models showed good accuracy without these temperature averages, their performance was more stable when robustness was assessed with them included. After developing the models, their robustness was evaluated through the sensitivity analysis described in Section 4.2.2. Figure 31 shows that the R-squared values, which measure goodness-of-fit, remain high across all cases, with the lowest being 0.84. This demonstrates that even with the introduction of artificial temperature measurement errors in the test dataset, the models still produce promising results in predicting process outcomes. Table 4 compares the performance of the models on the test dataset under three different conditions within Examples 1 and 2. It indicates that the models developed in the second example perform fairly well, and even with the introduction of errors in the test dataset, the results remain comparable to the ideal 66 condition scenario (Example 1), highlighting the robustness of the models designed to mimic real-life implementation. Table 4. Comparing the models’ performance on test datasets in Examples 1 and 2 Process Outcomes Conditions R-Squared MSE MAE Min DOC Ideal (Example 1) 0.94 2.35×10-6 8.75×10-4 With Incomplete dataset (Example 2- part 1) 0.96 1.43×10-6 8.90×10-4 Full Example 2 (with noise added part 2) 0.92 3.11×10-6 1.28×10-3 Uniformity Factor Ideal (Example 1) 0.95 2.26×10-7 2.94×10-4 With Incomplete dataset (Example 2- part 1) 0.94 2.38×10-7 2×10-4 Full Example 2 (with noise added part 2) 0.84 6.02×10-7 4.24×10-4 Gel Time Ideal (Example 1) 0.97 11 2.39 With Incomplete dataset (Example 2- part 1) 0.99 3.83 1.35 Full Example 2 (with noise added part 2) 0.97 10.28 2.43 Cure Time Ideal (Example 1) 0.97 14.29 2.20 With Incomplete dataset (Example 2- part 1) 1.00 2.13 1.06 Full Example 2 (with noise added part 2) 0.97 14.82 2.92 Temperature Overshoot With Incomplete dataset (Example 2- part 1) 0.96 1.47 0.67 Full Example 2 (with noise added part 2) 0.86 4.59 1.46 4.3 Example 3: Multi-Objective Design Optimization The objective of this example differs from the previous two; it aims to leverage the proposed AI methodology as a tool to assist manufacturers in optimizing (reverse engineering) the 67 curing process. The forward-looking models developed in earlier scenarios only assist manufacturers in predicting the performance of the cured part after the curing process, given the thermal camera measurements. The goal here is to establish design objectives and constraints (often conflicting in practice) to optimize the process outcome values based on the HTCs that need to be selected. As reviewed in Chapter 2, manufacturers are often unable to set the HTC values to an exact (nominal) number, and without sensors at every location of the autoclave, their value may become unknown [63], [64]. Nevertheless, having a backward-looking AI tool to determine the HTC values for a given process target can be highly beneficial. Although manufacturers might not be able to set HTCs to a specific value, understanding the current HTC and its optimal range allows them to adjust other related process parameters, such as air circulation, to steer these values toward the optimal range, thereby enhancing the curing process outcomes. However, to achieve this, another ML model needed to be developed first to predict the HTC values for a given sample. The multi-objective optimization process can then employ this model as part of its search. These steps are described next. 4.3.1 ANN Model to Predict HTCs In previous two scenarios, the time-series heatmap image was used to predict the process outcomes of various samples. These samples were generated through simulations, with variations in the top and bottom HTC values. Here, instead of using the part’s thermal history during curing to predict process outcomes, the goal is to predict HTC, a key factor in the curing process itself. Thus, this inverse ANN model now needs to predict two outputs: the top-side HTC and the bottom-side HTC. For samples/data collection, it was decided to use the datasets of Example 2 to be closer to the practical settings. Also, samples with a temperature overshoot of more than 10°C were removed, as such samples are deemed unsuitable for process optimization [64]. Removing these samples will help eliminate undesirable cases so the model can be trained on more optimal cases for this specific purpose in Example 3. Additionally, removing these samples allows the training model to be trained more efficiently, as the remaining samples exhibit a more visible pattern. Since, the removed samples primarily consisted of those with low HTC values, which are more influenced by exothermic reactions occurring within the part (see Section 2.2.3). Similar to example 2, the inputs used in this HTC-predicting ANN model are contrast, correlation, energy, dissimilarity, homogeneity, and the average temperatures at the top and bottom. 68 The developed ANN model utilized these seven inputs to predict the top-side HTC and bottom-side HTC simultaneously. 4.3.1.1 Results The following information is provided to evaluate the above model’s performance. Table 5 gives insights into the structure of the developed model and its performance on the test dataset after training, using MSE and MAE as evaluation metrics. Table 5. The ANN models description for the prediction of HTC values Process Outcome Hidden Layers and Neurons No. Epochs Data split MSE MAE Top HTC 3 (8 neurons each) 1730 102-34-34 127.13 9.44 Bottom HTC 32.58 4.11 Figure 32 shows the loss curve for both outputs during training. The loss function value combines both outputs; hence, only one loss function curve represents the training process. Figure 32. Loss function curves during ANN training for predicting HTC values Figure 33 presents the percentage error of the predicted HTC values compared to the actual values. Additionally, Figure 34 depicts the residuals of these predicted values relative to the actual values. These results are shown separately for both top-side HTC and bottom-side HTC, as they can be independently reported. Finally, Figure 35 compares the predicted HTC values with the actual values, with the R-squared value included to assess the goodness of fit. 69 Figure 33. Absolute error plots for ANN after training for predicting HTC values: (a) Top-side HTC, and (e) Bottom-side HTC Figure 34. Residual plots for ANN after training for predicting HTC values: (a) Top-side HTC, and (e) Bottom-side HTC Figure 35. R-Squared plots of predicting HTC values: (a) Top-side HTC, and (e) Bottom-side HTC 70 4.3.1.2 Discussion The reverse ANN model trained above shows better accuracy for the bottom-side HTC compared to the top-side HTC. This suggests that the model likely had more information correlating with the bottom-side HTC. The reason for this can be traced back to the case study’s description and the specific conditions under which the process undergoes the curing process. One possible explanation is that heat dissipation at the bottom responds more noticeably to changes in HTC. As seen in Figure 36, the bottom-side HTC demonstrates a more precise pattern compared to the average temperatures than the top-side HTC. This observation supports the idea that the bottom HTC has a more direct influence on temperature variations in those regions. Figure 36. The plots of average temperatures at the top and bottom compared to their respective HTC values However, the model's overall accuracy for both outputs (HTCs) are comparable, with a maximum error of 40% in their predictions. This indicates that the Haralick features for both outputs contain reasonably sufficient information to yield acceptable prediction results. The maximum residual for the top-side HTC is ±20 𝑊𝑚2𝐾, while it’s ±15 𝑊𝑚2𝐾 for the bottom-side HTC. Consequently, although Figure 35 illustrates that the model effectively fits the inputs to the 71 outputs, from an engineering judgment perspective and given the intended use of this model, it should be employed as an estimator of the HTC range rather than a precise value. Therefore, the provided HTC values should be used as a range after determining the optimal case through a genetic algorithm. Further simulation can then be employed to pinpoint the exact values within this range. In other words, using the estimated values and the possible error range, trial and error through simulation will reveal the actual conditions that produce the optimal process outcomes. This concept is further elaborated in the following section. 4.3.2 Optimization Problem With models that can now predict both the process outcomes (i.e., similar to Example 2) and the conditions that lead to them (i.e., the model in section 4.3.1), the design optimization example can be defined. The first step in this process is setting specific objectives, which describe the goals of the design. These goals often have conflicting trends, where improving one may make another less desirable, creating a need for multi-objective optimization. Additionally, these objectives must be met within specific guidelines, which necessitates defining the constraints of the optimization process. The optimization solution was conducted using a genetic algorithm (namely NSGA-II, which will be further discussed in Section 4.3.2.3). As outlined in Chapter 3, genetic algorithms generally operate on the principle of natural selection, where the fittest individuals are selected for reproduction to produce the next generation. By applying specific objectives and constraints, the algorithm iteratively improves upon the existing samples [156], [157]. This approach enables the creation of an optimal case by combining and refining the most favourable characteristics of superior samples, thereby driving the solution toward the best possible outcome. This means that the genetic algorithm utilizes the superior samples to iteratively refine the solution, combining and evolving these samples to maximize the desired outcomes. Through this process, the algorithm converges on a set of conditions that provide the best balance between competing objectives, ensuring that the most favourable results are achieved while subjected to the defined constraints. 4.3.2.1 Constraint Functions In an optimization problem, constraints define the boundaries within which the objectives must be achieved. If a sample falls outside these constraints, it is considered undesirable and should not be regarded as an optimal case. The constraints set for this example are set based on the existing 72 expert knowledge of the curing process and desirable outcomes. For instance, during the optimization process using the genetic algorithm, any case with an HTC value exceeding 150 is excluded, as previous studies suggest that HTC (for both sides) in an autoclave typically does not surpass this threshold [60], [64]. Another important constraint is that the minimum degree of cure (DOC) should ideally be above 0.9, depending on the manufacturer's specific requirements. This value is generally preferred as it ensures better material properties. Additionally, the part's temperature overshoot must remain below 10°C to prevent defects caused by excessive temperature; see also Eq. (18) for the mathematical representation. 4.3.2.2 Objective Functions Theoretically, the inverse optimization process design should improve all the process outcomes involved. Primarily, from a composite engineering point of view, the minimum degree of cure should be of top priority and maximized to ensure better part properties. At the same time, the cure uniformity factor should be minimized to achieve a more uniform distribution of DOC and prevent potential defects (see section 2.3.2 for the definition of this factor). Moreover, the gel and cure time must be reduced to minimize energy consumption and shorten the curing process. These objectives may or may not conflict with each other, as improving one could make another less desirable, or they might align (be proportional) without conflict. To assess this, Figure 37 provides their linear correlation heatmap to capture these relationships. Moreover, Figure 38 depicts the pairwise comparison between min DOC and the rest of process outcomes for the samples that meet all the defined optimization constraints, similar to Figure 37. The results indicate that only the uniformity factor conflicts with the other outcomes. The data suggests that, generally, gel time and cure time decrease as the minimum DOC increases, which is desirable. However, the uniformity factor increases as the minimum DOC increases, revealing a trade-off between them. A higher uniformity factor means a less consistent DOC profile across thickness, which is not desirable for designers. Consequently, the goal of the genetic algorithm is to find a balance between these conflicting objectives, identifying a case where both the minimum DOC and uniformity itself are optimized despite their trade-off; see also Eq. 18 for the mathematical representation. 73 Figure 37. Pairwise correlation heatmap between the process outcomes for samples within the optimization constraints Figure 38. Pairwise comparison between min DOC and the rest of process outcomes for samples within the optimization constraints 4.3.2.3 NSGA-II Solution Algorithm As mentioned before, a genetic algorithm was employed to generate the optimal solution. The specific genetic algorithm used in this study was NSGA-II (Non-dominated Sorting Genetic Algorithm II). NSGA-II is known for its efficiency in solving multi-objective optimization 74 problems when objectives are in conflict. The main idea of NSGA-II is to generate a set of optimal solutions, known as the Pareto front, where no solution can be improved in one objective without compromising another [158], [159]. NSGA-II operates by first initializing or using a population of potential solutions and then iteratively improving them through selection, crossover, and mutation processes. The algorithm has a fast non-dominated sorting approach to rank solutions based on their performance across all set objectives. It also uses a crowding distance mechanism to keep diversity among solutions, preventing premature convergence to a single point [159]. A similar study applied NSGA-II to optimize curing profiles for thick composite laminates in the autoclave curing process, successfully balancing the trade-off between minimizing curing time and ensuring product quality [160]. The study demonstrated NSGA-II’s capability to address multiple objectives simultaneously, showcasing its effectiveness in autoclave optimization scenarios. This supports the choice of NSGA-II in the current study, where it is used to balance the conflicting objectives involved in the curing process, like maximizing the minimum degree of cure while also trying to decrease the uniformity factor of DOCs across the part thickness. The final result is a set of Pareto-optimal solutions that provide manufacturers with various choices, allowing them to pick the best compromise between the competing objectives based on their specific needs and limitations. To summarize, a set of constraints and objectives were defined to initiate the genetic algorithm. These were decided based on the existing knowledge of the process and the intention of improving the process outcomes. They were as follows: 𝑭𝒊𝒏𝒅; (𝑿) = (𝐶𝑜𝑛𝑡𝑟𝑎𝑠𝑡, 𝐶𝑜𝑟𝑟𝑒𝑙𝑎𝑡𝑖𝑜𝑛, 𝐸𝑛𝑒𝑟𝑔𝑦, 𝐷𝑖𝑠𝑠𝑖𝑚𝑖𝑎𝑙𝑟𝑖𝑡𝑦, 𝐻𝑜𝑚𝑜𝑔𝑒𝑛𝑖𝑡𝑦, 𝑇_𝑚𝑒𝑎𝑛𝑇𝑜𝑝, 𝑇_𝑚𝑒𝑎𝑛𝐵𝑜𝑡𝑡𝑜𝑚) 𝑴𝒂𝒙𝒊𝒎𝒊𝒛𝒆 𝑀𝑖𝑛 𝐷𝑂𝐶, 𝑴𝒊𝒏𝒊𝒎𝒊𝒛𝒆 𝑈𝑛𝑖𝑓𝑜𝑟𝑚𝑖𝑡𝑦 𝐹𝑎𝑐𝑡𝑜𝑟 𝑜𝑓 𝐷𝑂𝐶 𝑺𝒖𝒃𝒋𝒆𝒄𝒕𝒆𝒅 𝒕𝒐: 𝑀𝑖𝑛 𝐷𝑂𝐶(𝑋) ≥ 0.9, 𝑇𝑒𝑚𝑝𝑒𝑟𝑎𝑡𝑢𝑟𝑒 𝑂𝑣𝑒𝑟𝑠ℎ𝑜𝑜𝑡(𝑋) ≤ 10℃, 𝑇𝑜𝑝 & 𝐵𝑜𝑡𝑡𝑜𝑚 𝐻𝑇𝐶(𝑋) ≤ 150 𝑊𝑚2𝐾 (18) Eq.18 indicates that the NSGA-II algorithm attempts to find a set of values for the Haralick texture features and average temperature at the top and bottom (as X vector), that can maximize the DOC and minimize the uniformity factor (the objectives), subject to the given constraints. NSGA-II algorithm was performed through Pymoo, an open-source framework for multi-objective 75 optimization [161]. To recap, in the NSGA-II optimization, the algorithm generates a set of values for X, which is then used by the model developed in Section 4.2.1 to predict the values of the uniformity factor, minimum DOC, and temperature overshoot. These values are predicted based on the corresponding X values. Additionally, X is also used to predict the top and bottom HTC values using the ANN developed in Section 4.3.1. In other words, for each particular X generated, the corresponding process outcomes and HTC values are predicted using surrogate models (ANN models). Once all necessary process outcomes are predicted, the genetic algorithm evaluates whether the generated X satisfies the objectives while staying within the constraints. In implementation of NSGA-II, the crossover and mutation operators play an essential role in maintaining diversity and guiding the search for optimal solutions. The crossover method used was Simulated Binary Crossover (SBX), which aims to mimic natural reproduction by combining pairs of solutions to generate offspring with new characteristics [162]. With a 90% crossover probability, the SBX operator encourages significant exploration of the solution space while preserving essential traits of the parents. The mutation process, on the other hand, is implemented by Polynomial Mutation, which slightly alters individual solutions to introduce new variations, avoiding local optima [163]. This mutation is controlled by a 10% probability, ensuring that while new possibilities are explored, the algorithm avoids random or excessive deviations. The initial population to start the genetic algorithm was chosen among multiple samples in the case study that satisfied the given constraints. Together, these mechanisms let the algorithm effectively navigate the trade-offs between conflicting objectives, ensuring that the final solutions are both diverse and optimal. 4.3.2.4 Pareto Front Solutions As discussed above, the output of the NSGA-II algorithm is a set of Pareto-optimal solutions, where no single solution can be improved in one objective without compromising another. This means none of the solutions on the Pareto front are strictly better than any other. For instance, no solution in this Pareto front has both a smaller DOC and a higher uniformity factor than another solution (non-dominated). Using the described NSGA-II algorithm, the solutions displayed in Figure 39 were identified as the Pareto front solutions (the marked numbered solutions will be explained in section 4.3.2.5) 76 Figure 39. Pareto front solutions created by the genetic algorithm for Example 3 (for numbered/ranked points, refer to Table 6) 4.3.2.5 TOPSIS Multi-Criteria Decision Making The Pareto front shown in Figure 39 presents a set of multiple alternatives that will be suggested to the manufacturer as optimal solutions. However, the manufacturer needs to select a set of top-ranked solutions to adjust the curing process conditions as needed. This selection process is a form of multi-criteria decision-making (MCDM). To facilitate this, a method is required to weigh multiple criteria according to their importance and identify the optimal solution. This study selected TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) for this purpose. TOPSIS is a good option in such situations because it identifies the solution that is closest to the ideal solution and farthest from the worst possible outcome [164]. By assigning equal weights (0.5) to both the minimum DOC and the uniformity factor, the method reflects the need to balance these two objectives by maximizing the degree of cure while decreasing uniformity factor. This approach ensures that the final selection from the Pareto front aligns with both technical requirements and manufacturing priorities, providing a practical and balanced solution for process optimization. Table 6 presents the top three choices for applying the TOPSIS method to the Pareto-front alternative solutions, along with the poorest-ranking alternative, showcasing the significant performance difference between the best and worst solutions. These selections, shown in Figure 39, represent the most balanced outcomes among the 50 optimal solutions identified by the NSGA-II algorithm. The values in this table are predicted through the ANN models. 77 Table 6. The result of TOPSIS MCDM on Pareto front solutions in Example 3 TOPSIS Ranking (Design alternatives) Top HTC 𝐖𝐦𝟐𝐊 Bottom HTC 𝐖𝐦𝟐𝐊 Min DOC criterion Uniformity Factor criterion Performance Score % 1 113.9 120.4 0.905 0.00146 65.5 2 129.7 96.4 0.906 0.00155 65 3 102.3 138.4 0.906 0.00153 64.8 . . . . . . . . . . . . . . . . . . 50 100.4 75.9 0.908 0.00213 48.1 It is important to note in Table 6 that the Pareto front optimal solutions show less significant variation in the minimum DOC criterion, compared to the uniformity factor. This can be attributed to two main reasons. First, a constraint is applied to the minimum DOC with the GA optimization (Eq. (18)), removing all alternative designs with DOC values below 0.9, as often required by designers. This restriction effectively narrows the range of possible values for the minimum DOC. Secondly, while top and bottom HTCs influence the minimum DOC to a certain extent, their impact is more pronounced on ‘differential’ variables like the uniformity factor where e.g. the difference between the bottom-region DOC and top-region DOC would sizably be affected by the corresponding HTC values. 4.3.2.6 Discussion The TOPSIS method, when combined with predictive models for process outcomes and their corresponding HTC values in the NSGA-II algorithm, determines the top three optimal solutions. However, due to potential errors between the HTC values and process outcomes reported by the predictive models, a manual check is necessary to verify the actual outcomes corresponding to these HTC values. The top three solutions identified by TOPSIS show very similar performance scores, with less than a 1% difference. One approach is to use the HTC values from these solutions to test them in the RAVEN simulation, ultimately choosing the case that yields results closest to those predicted by the GA and ANN models. Considering the possible offset between the actual and predicted values for the corresponding process outcomes of their reported HTCs, a trial-and-error approach can be adopted 78 within the RAVEN environment. Using simulations, the top three solutions from TOPSIS yielded promising results. Table 7 compares the outcomes corresponding to those HTC values in RAVEN with the values predicted by the ANN models, allowing us to identify which option produces the closest results. Table 7. Comparing results of NSGA-II-TOPSIS with the corresponding samples through ground-truth simulations in Example 3 TOPSIS- Ranked Top Three Design Alternatives Model Min DOC (+) Uniformity Factor (-) 1 ANN 0.905 0.001 Simulation 0.904 0.001 Error% 0.110 7.00 2 ANN 0.906 0.001 Simulation 0.904 0.001 Error% 0.220 1.970 3 ANN 0.906 0.001 Simulation 0.904 0.001 Error% 0.220 5.670 Although the simulations confirm that the ANN predictions are reasonably accurate, the predicted process outcome values from the second-ranked TOPSIS solution were the closest to the ground-truth simulation results among the TOPSIS choices. Given the small differences in performance scores and the accuracy of this option, the second-ranked solution is selected as the targeted HTC for process optimization. Notably, the simulation shows that the second-ranked option actually has a lower uniformity factor, indicating better uniformity, which further supports its selection. Despite the errors in predicting the exact values, the simulation results, follow the same trend as the predicted values. These errors are within the expected range, as illustrated by the ANN model performance in Figure 26, Figure 27. For example, in the chosen solution, the uniformity factor from the simulation shows a 1.97% error, aligning with the range observed in Figure 26. Moreover, all error values are below 7%, underscoring the accuracy of the proposed methodology in this research. 79 Additionally, an alternative trial-and-error approach could involve adjusting HTC values within the range shown in Figure 34, focusing on finding the actual HTC values that yield the exact or near-exact desired outcomes. However, given the minimal performance score differences and low error rates in this case, comparing the top solutions' ANN predictions with simulation results was sufficient to identify an optimal solution with corresponding HTC values. Overall, this demonstrates the potential of the suggested optimization method in helping manufacturers optimize the curing process. Although manual effort is still required to identify the exact conditions of the optimal solution, this approach offers valuable insight into what the optimal parameters might be. By providing process designers with a clear direction, the suggested optimization method serves as a practical tool, assisting manufacturers in identifying the optimal solution to make informed adjustments to modify HTC values. 80 Chapter 5: Conclusions, Limitations, and Future Work Recommendations 5.1 Conclusions Composite materials are integral to high-performance industries such as aerospace, automotive, and construction due to their high strength-to-weight ratios and tailorablity to specific design requirements. Despite their benefits, manufacturing composite materials requires careful monitoring and control to ensure the final product’s quality and reliability. The significance of this industry sector underscores the importance of optimizing manufacturing processes. One critical stage is the curing process, which transforms composites from a pliable state into a rigid, durable form, significantly influencing their mechanical properties and overall performance. For industrial parts, curing typically occurs in an autoclave, where heat and pressure are applied. The parameters affecting the heat transfer process, such as temperature, pressure, and heat transfer coefficient (HTC), are vital in determining the product’s performance. Variations in these parameters can cause considerable deviations in the properties of the cured composite part, affecting its overall quality. Traditional methods of monitoring the curing process of composites often fail to provide the immediate feedback necessary for quick adjustments on-site and are not always cost-effective. These limitations make ensuring consistent, high-quality composite materials challenging, particularly in industrial settings where time is crucial. This highlights the need for advanced monitoring systems that offer continuous, accurate process feedback, enabling manufacturers to produce reliable composite materials without time-consuming and costly experiments. This thesis aimed to address these challenges by exploring the potential utility of Artificial Neural Networks (ANNs) in predicting autoclave curing process outcomes using thermal camera images. The goal was to develop and test a fast design tool that not only reports the process outcomes but also can be used to optimize the process by leveraging ANN models developed and genetic algorithms. This approach may enable manufacturers to enhance composite product properties by adjusting cure process parameters in a less costly yet reliable fashion. Chapter 2 presented a literature review identifying the potential of using neural network models in conjunction with Haralick texture features as a promising approach for evaluating autoclave curing outcomes. This chapter also discussed the complexity of the curing process, highlighting how factors such as temperature, pressure, and HTC variations can lead to different outcomes. Chapter 3 detailed the methodology and the selected case study, focusing on curing 81 HEXCEL 8552-1/IM7 composite on Invar 36 tooling with varying HTC values. The methodology was divided into three scenarios: one to demonstrate the approach’s potential under ideal conditions, another to assess its effectiveness in a more practical setting, plus a sensitivity analysis to determine the robustness of the models. And lastly, a scenario in which the developed models through this method are to be used in process optimization. Chapter 4 presented the results, discussing the essential findings and their implications. The main conclusions from this research are as follows: From Application Example 1 (i.e., modelling with complete and noise-free data): o The pairwise correlation assessment of Haralick features revealed that contrast and dissimilarity, ASM, and energy have correlation coefficients of 1, indicating perfect correlation. Contrast also has a correlation coefficient of -1 with homogeneity, indicating a perfectly inverse linear relationship. Consequently, selecting contrast, correlation, and ASM alone captured the essential information provided by the Haralick features in this scenario. o The trained models demonstrated no signs of overfitting or underfitting, highlighting the effectiveness of techniques such as early stopping and weight saving. o The predictions were most accurate for the minimum degree of cure (DOC) through thickness, with a top prediction error of 0.7%. The gel and cure time models also performed well, with maximum errors of around 4%. However, the model for predicting the uniformity factor was less accurate, with a maximum prediction error of 50%, though over 90% of data points had errors below 20%. Overall, they showed acceptable results for all models. o The residuals of the predictions for gel time and cure time maxed at 10 minutes. However, over 90% of predictions’ residuals mainly were under 5 minutes. This indicates a tolerance of ±10 minutes, which manufacturers might find acceptable depending on their specific requirements. o Plots of actual (observed) versus predicted values showed high accuracy of predictions, with R-squared values exceeding 0.94 for both the complete and test datasets, further supporting the absence of overfitting or underfitting. 82 o SHapley Additive exPlanations (SHAP) values indicated that contrast was a highly informative Haralick feature, playing a critical role across all models. This suggests a strong correlation between contrast and process outcomes that align with the actual trend within the data. These correlations align with the formulas and definitions of the Haralick features where higher contrast values reflected greater temperature changes and, hence, a more effective cure process. o The results suggest that the selected Haralick features contain sufficient information to predict process outcomes using ANN under ideal conditions accurately. From Application Example 2 (i.e., modelling with incomplete and noisy data): o Constructing the time-series heatmap images required fewer measurements during the curing process, as the images were generated using temperatures from specific time intervals. A new grayscale with larger increments was also developed to accommodate significant temperature changes, reducing the impact of measurement errors. o Pairwise correlation analysis showed that only ASM and energy had a high correlation, as expected. The selected inputs for the models included contrast, correlation, energy, dissimilarity, homogeneity, and the average temperatures at the top and bottom, enhancing robustness against measurement uncertainties. o The models developed for this scenario also showed no sign of overfitting and underfitting when evaluating their loss function curves through the training process of the models. o Prediction errors for minimum DOC were within 1%, while the uniformity factor and temperature overshoot had maximum errors of 70%, with 90% of predictions having errors below 10% and 20%, respectively. Gel time and cure time errors were under 6% and 1.6%, respectively, indicating that the models performed comparably or better than the ideal condition scenario. o Residuals for gel time and cure time predictions were mostly within ±5 minutes, and temperature overshoot predictions were within ±2℃, demonstrating decent accuracy. 83 o The plots of actual values of the process outcomes compared to the predicted values showed goodness of fit across all models, with the lowest R-squared value of 0.96 for the whole dataset across all models and the lowest of 0.94 for the test dataset for all the predicted outcomes. o A key difference from the previous scenario was the inclusion of average temperatures at the top and bottom during curing, which proved beneficial in helping the models learn patterns. SHAP plots confirmed that these averages contributed to model performance alongside Haralick texture features. These plots also showed that the models still benefitted considerably from the information provided by the Haralick image texture features. Contrast or correlation is always present in the top three contributing factors to the models’ learning process. o The addition of temperature measurement uncertainties to the test dataset resulted in a new set of samples with varying inputs but the same outputs. Re-evaluating all the models on this modified data revealed consistently high R-squared values, with the lowest being 0.84. The most significant drop in performance was observed for the uniformity factor, with the R-squared value decreasing from 0.94 to 0.84, indicating a 10% decrease in performance. This underscores the models’ robustness, as they continue to yield promising results in predicting process outcomes even when artificial temperature measurement errors are introduced. From Application Example 3 (i.e. multi-objective design optimization/reverse engineering): o The model predicting HTCs at the top and bottom showed no signs of overfitting or underfitting, with better accuracy for the bottom-side HTC, likely due to more precise patterns in the data. Although the maximum error was 40%, the results were still acceptable, with R-squared values of 0.88 or higher. The maximum residual for the top-side HTC is ±20 Wm2K, while it’s ±15 Wm2K for the bottom-side HTC. Although the R-squared values illustrate that the model effectively fits the inputs to the outputs, from an engineering judgment perspective and given the intended use of this model, it should be employed as an estimator of the HTC range rather than a precise value. 84 o The developed models were used in an NSGA-II genetic algorithm to conduct process optimization. The constraints were chosen based on the actual operating conditions of the process. The main goal of the optimization was to achieve a balance between increasing the minimum DOC and ensuring uniformity. Upon analyzing the dataset samples, it was observed that an increase in the minimum value of DOC led to a decrease in the uniformity of the degree of cure through the thickness, which is not desirable. o By applying TOPSIS decision making model to the Pareto solutions, the second-ranked case with a top-side HTC of 129.7 Wm2K and a bottom-side HTC of 96.4 Wm2K demonstrated the closest alignment with the ground-truth simulation results. Despite minor discrepancies, with a 0.22% difference in minimum DOC and a 1.97% difference in the uniformity factor, this solution was chosen as the optimum target. o The effectiveness of the GA Pareto solutions, coupled with the minimal errors observed in predicted values of ANNs, highlights the potential of this optimization framework for enhancing the autoclave curing process. 5.2 Limitations and Future Work Recommendations The limitations and future recommendations of this study are summarized below: • All the samples (synthetic data) used in this case study were created using 1D simulations in RAVEN. While the assumption of 1D simulation accuracy has proven reliable based on the given explanations in Chapter 3 and past studies, it is clear that no 1D cross-section exists in real life. However, the same approach may be applied to a real-life 2D cross-sectional area. One suggestion is to divide the cross-sectional area into small vertical strips, which can each be assessed individually through the ANN models. This would allow for predictions of each strip’s characteristics. For example, by predicting the minimum degree of cure (DOC) for each of these smaller sections, the lowest value would be reported as the minimum DOC for the entire part. 85 • The limitations of the simulation geometry are not confined to 2D cross sections. Recognizing that real-life parts are 3D objects, with HTC variability throughout their surface, it is necessary to modify the implementation of these ANN models. The temperatures must somehow be estimated for any point within the part, such as a location in the middle where the cross-section is not visible from the outside. One approach is to capture the temperatures of any visible cross section and the surface heatmap of the part. Using heat transfer relations, surface temperatures, and visible thicknesses, it may be possible to map the temperature distribution within the part’s interior artificially. Once this challenge is addressed, the same strip-based approach can be applied to each cross-sectional area. • In this research, the samples used to train and assess the ANN models were collected through simulation, given the need for an extensive dataset, which would be costly and time-consuming to generate through physical experiments. However, future studies could include real-life testing and sample creation to train models on real-life data or evaluate their performance on physical samples. • For this study, only variations in HTC were used to create samples for the case studies; thus, the models were trained on scenarios where HTC was the only variable. However, other factors, such as the cure cycle, part thickness or geometry, might vary in real-life scenarios. Therefore, it is recommended for future studies to implement the proposed methodology in this research on case studies involving changes in other factors. • The performance of the ANN models was improved throughout this research by implementing strategies such as increasing model complexity and data augmentation (e.g., top and bottom average temperatures in Example 2). However, some outcomes were still not predicted as accurately as others. For instance, the prediction of the DOC uniformity factor showed a higher error compared to other outcomes in example 2. Exploring new texture features from different methods or testing alternative ML models may help improve the accuracy of these process outcomes. • The prediction of HTC values was also less accurate compared to other outcomes. Since changes in HTC values in real-life scenarios might lead to significantly different outcomes 86 from an engineering perspective, these predictions should be considered more as a guide to identify the potential range of actual HTC values rather than as precise estimates. Despite attempts to improve prediction accuracy, such as using different ML models like XGBOOST or adding new texture features, no significant improvements were observed. For future research, exploring new solutions or providing additional information that could lead to more accurate HTC predictions is highly recommended, thereby eliminating the need for manual interventions in the final stage. • The methodology developed in this research may be adapted for use in other composite manufacturing processes. Specifically, the approach of utilizing video data captured during the manufacturing process to predict different outcome variables can be applied beyond autoclave curing. For instance, in a thermoforming composite manufacturing setup, similar video-based AI analysis could be used to predict potential defects in the final product. • The methodology developed in this study could potentially be adapted for practical use in different autoclave processing conditions through cost-effective re-training of the ANN models using RAVEN simulations. The present ANN models, initially trained on synthetic data, might also be fine-tuned using transfer learning (TL) to better align with each specific operational setup. This could involve updating the pre-trained models with additional data collected from specific factory’s design cases and curing parameters. Additionally, exploring the combination of transfer learning with other advanced feature extraction methods, such as CNN-based features might further enhance the model’s predictive accuracy while reducing the volume of new data required for retraining. 87 Bibliography [1] J. S. Jayan, S. Appukuttan, R. Wilson, K. Joseph, G. George, and K. Oksman, “1 - An introduction to fiber reinforced composite materials,” in Fiber Reinforced Composites, K. Joseph, K. Oksman, G. George, R. Wilson, and S. Appukuttan, Eds., in Woodhead Publishing Series in Composites Science and Engineering. , Woodhead Publishing, 2021, pp. 1–24. doi: https://doi.org/10.1016/B978-0-12-821090-1.00025-9. [2] B. Parveez, M. I. Kittur, I. A. Badruddin, S. Kamangar, M. Hussien, and M. A. Umarfarooq, “Scientific Advancements in Composite Materials for Aircraft Applications: A Review,” Polymers (Basel), vol. 14, no. 22, 2022, doi: 10.3390/polym14225007. [3] M. R. M. Asyraf et al., “Advanced Composite in Aerospace Applications: Opportunities, Challenges, and Future Perspective,” in Advanced Composites in Aerospace Engineering Applications, N. Mazlan, S. M. Sapuan, and R. A. Ilyas, Eds., Cham: Springer International Publishing, 2022, pp. 471–498. doi: 10.1007/978-3-030-88192-4_24. [4] J. Fleischer, R. Teti, G. Lanza, P. Mativenga, H.-C. Möhring, and A. Caggiano, “Composite materials parts manufacturing,” CIRP Annals, vol. 67, no. 2, pp. 603–626, 2018, doi: https://doi.org/10.1016/j.cirp.2018.05.005. [5] S. Y. Park and W. J. Choi, “Production control effect on composite material quality and stability for aerospace usage,” Advanced Composite Materials: Properties and Applications, pp. 112–194, 2017. [6] J. Meegan, “Some of the challenges faced by the Composites Industry in its bid to become more sustainable,” RSC Sustainability, vol. 1, no. 7, pp. 1737–1742, 2023. 88 [7] W. T. Tengfei Chang Lihua Zhan and S. Li, “Void content and interfacial properties of composite laminates under different autoclave cure pressure,” Compos Interfaces, vol. 24, no. 5, pp. 529–540, 2017, doi: 10.1080/09276440.2016.1237113. [8] F. Y. C. Boey and S. W. Lye, “Void reduction in autoclave processing of thermoset composites: Part 1: High pressure effects on void reduction,” Composites, vol. 23, no. 4, pp. 261–265, 1992, doi: https://doi.org/10.1016/0010-4361(92)90186-X. [9] T. Bohne, T. Frerich, J. Jendrny, J.-P. Jürgens, and V. Ploshikhin, “Simulation and validation of air flow and heat transfer in an autoclave process for definition of thermal boundary conditions during curing of composite parts,” J Compos Mater, vol. 52, no. 12, pp. 1677–1687, 2018. [10] H. Koushyar, S. Alavi-Soltani, B. Minaie, and M. Violette, “Effects of variation in autoclave pressure, temperature, and vacuum-application time on porosity and mechanical properties of a carbon fiber/epoxy composite,” J Compos Mater, vol. 46, no. 16, pp. 1985–2004, 2012, doi: 10.1177/0021998311429618. [11] S. Shanmuganathan, “Artificial Neural Network Modelling: An Introduction,” in Artificial Neural Network Modelling, S. Shanmuganathan and S. Samarasinghe, Eds., Cham: Springer International Publishing, 2016, pp. 1–14. doi: 10.1007/978-3-319-28495-8_1. [12] O. I. Abiodun et al., “Comprehensive Review of Artificial Neural Network Applications to Pattern Recognition,” IEEE Access, vol. 7, pp. 158820–158846, 2019, doi: 10.1109/ACCESS.2019.2945545. 89 [13] S. Kumar et al., “Machine learning techniques in additive manufacturing: a state of the art review on design, processes and production control,” J Intell Manuf, vol. 34, no. 1, pp. 21–55, 2023, doi: 10.1007/s10845-022-02029-5. [14] M. Javaid, A. Haleem, R. P. Singh, R. Suman, and E. S. Gonzalez, “Understanding the adoption of Industry 4.0 technologies in improving environmental sustainability,” Sustainable Operations and Computers, vol. 3, pp. 203–217, 2022, doi: https://doi.org/10.1016/j.susoc.2022.01.008. [15] H. Fu, Y. Qin, X. He, X. Meng, Y. Zhong, and Z. Zou, “Effect of curing degree on mechanical and thermal properties of 2.5D quartz fiber reinforced boron phenolic composites,” E-Polymers, vol. 19, no. 1, pp. 462–469, 2019, doi: 10.1515/epoly-2019-0048. [16] J. Liang et al., “Experimental Study of Curing Temperature Effect on Mechanical Performance of Carbon Fiber Composites with Application to Filament Winding Pressure Vessel Design,” Polymers (Basel), vol. 15, no. 4, Feb. 2023, doi: 10.3390/polym15040982. [17] O. A. Ekuase, N. Anjum, V. O. Eze, and O. I. Okoli, “A Review on the Out-of-Autoclave Process for Composite Manufacturing,” Journal of Composites Science, vol. 6, no. 6, 2022, doi: 10.3390/jcs6060172. [18] T. B. Hudson and F.-G. Yuan, “Automated In-Process Cure Monitoring of Composite Laminates Using a Guided Wave-Based System With High-Temperature Piezoelectric Transducers,” J Nondestruct Eval Diagn Progn Eng Syst, vol. 1, no. 2, pp. 021008-021008–8, Feb. 2018, doi: 10.1115/1.4039230. 90 [19] S. Karimullah, M. M. Basha, F. Shaik, S. Gundala, A. Stan, and O. P. Stan, “Advanced Brain Tumor Diagnosis using Haralick Texture Features and Multiclass SVM in MRI Imaging,” in 2024 IEEE International Conference on Automation, Quality and Testing, Robotics (AQTR), 2024, pp. 1–6. doi: 10.1109/AQTR61889.2024.10554101. [20] E. P. Koumoulos et al., “Research and Development in Carbon Fibers and Advanced High-Performance Composites Supply Chain in Europe: A Roadmap for Challenges and the Industrial Uptake,” Journal of Composites Science, vol. 3, no. 3, 2019, doi: 10.3390/jcs3030086. [21] S.-J. Park and M.-K. Seo, “Chapter 7 - Types of Composites,” in Interface Science and Composites, vol. 18, S.-J. Park and M.-K. Seo, Eds., in Interface Science and Technology, vol. 18. , Elsevier, 2011, pp. 501–629. doi: https://doi.org/10.1016/B978-0-12-375049-5.00007-4. [22] R. K. Dhir, J. de Brito, R. Mangabhai, and C. Q. Lye, “3 - Production and Properties of Copper Slag,” in Sustainable Construction Materials: Copper Slag, R. K. Dhir, J. de Brito, R. Mangabhai, and C. Q. Lye, Eds., Woodhead Publishing, 2017, pp. 27–86. doi: https://doi.org/10.1016/B978-0-08-100986-4.00003-1. [23] M. A. Azmah Hanim, D. Brabazon, and M. S. J. Hashmi, “8 - Cracks, microcracks, and fracture toughness of polymer composites: Formation, testing method, nondestructive detection, and modifications,” in Failure Analysis in Biocomposites, Fibre-Reinforced Composites and Hybrid Composites, M. Jawaid, M. Thariq, and N. Saba, Eds., in Woodhead Publishing Series in Composites Science and Engineering. , Woodhead 91 Publishing, 2019, pp. 157–180. doi: https://doi.org/10.1016/B978-0-08-102293-1.00008-5. [24] S. B. Singh, S. Vummadisetti, and H. Chawla, “Influence of curing on the mechanical performance of FRP laminates,” Journal of Building Engineering, vol. 16, pp. 1–19, 2018, doi: https://doi.org/10.1016/j.jobe.2017.12.002. [25] M. Gupta, A. Tyagi, and M. Raula, “Effect of Curing Time on Physico-mechanical Properties on Dental Composite,” Journal of Polymer & Composites, vol. 10, pp. 36–47, Jul. 2022. [26] M. Mehdikhani, L. Gorbatikh, I. Verpoest, and S. V Lomov, “Voids in fiber-reinforced polymer composites: A review on their formation, characteristics, and effects on mechanical performance,” J Compos Mater, vol. 53, no. 12, pp. 1579–1669, 2019, doi: 10.1177/0021998318772152. [27] . Sudarisman and I. Davies, “Influence of Compressive Pressure, Vacuum Pressure, and Holding Temperature Applied during Autoclave Curing on the Microstructure of Unidirectional CFRP Composites,” Adv Mat Res, vol. 41–42, pp. 323–328, Jul. 2008, doi: 10.4028/www.scientific.net/AMR.41-42.323. [28] L. S. Vázquez, J. López-Beceiro, A.-M. Díaz-Díaz, A. Álvarez-García, M. Pereira, and R. Artiaga, “Comparison by thermal analysis of Joule-cured versus oven-cured composites,” J Therm Anal Calorim, 2024, doi: 10.1007/s10973-024-13002-0. [29] V. Drakonakis, J. Seferis, and C. C. Doumanidis, “Curing Pressure Influence of Out-of-Autoclave Processing on Structural Composites for Commercial Aviation,” Advances in Materials Science and Engineering, vol. 2013, Jul. 2013, doi: 10.1155/2013/356824. 92 [30] R. A. Witik, F. Gaille, R. Teuscher, H. Ringwald, V. Michaud, and J.-A. E. Månson, “Economic and environmental assessment of alternative production methods for composite aircraft components,” J Clean Prod, vol. 29–30, pp. 91–102, 2012, doi: https://doi.org/10.1016/j.jclepro.2012.02.028. [31] M. G. Collinson, M. P. Bower, T. J. Swait, C. P. Atkins, S. A. Hayes, and B. Nuhiji, “Novel composite curing methods for sustainable manufacture: A review,” Composites Part C: Open Access, vol. 9, p. 100293, 2022, doi: https://doi.org/10.1016/j.jcomc.2022.100293. [32] D. Silva, R. Rocha, F. Ribeiro, and H. Monteiro, “Environmental Impact of an Innovative Aeronautic Carbon Composite Manufactured via Heated Vacuum-Assisted Resin Transfer Molding,” Sustainability, vol. 16, no. 8, 2024, doi: 10.3390/su16083253. [33] R. Tu, T. Liu, K. Steinke, J. Nasser, and H. A. Sodano, “Laser induced graphene-based out-of-autoclave curing of fiberglass reinforced polymer matrix composites,” Compos Sci Technol, vol. 226, p. 109529, 2022, doi: https://doi.org/10.1016/j.compscitech.2022.109529. [34] G. Marsh, “‘De-autoclaving’ prepreg processing,” Reinforced Plastics, vol. 56, no. 5, pp. 20–25, 2012, doi: https://doi.org/10.1016/S0034-3617(12)70108-X. [35] J. N. Hay and P. O’Gara, “Recent developments in thermoset curing methods,” Proc Inst Mech Eng G J Aerosp Eng, vol. 220, no. 3, pp. 187–195, 2006, doi: 10.1243/09544100JAERO35. 93 [36] D. Abliz, Y. Duan, L. Steuernagel, L. Xie, D. Li, and G. Ziegmann, “Curing Methods for Advanced Polymer Composites - A Review,” Polymers and Polymer Composites, vol. 21, no. 6, pp. 341–348, 2013, doi: 10.1177/096739111302100602. [37] S. Luo, “Radiation Processing,” in Nuclear Science and Technology: Isotopes and Radiation, S. Luo, Ed., Singapore: Springer Nature Singapore, 2023, pp. 183–262. doi: 10.1007/978-981-99-3087-6_5. [38] P. K. Kumar, N. V Raghavendra, and B. K. Sridhara, “Optimization of infrared radiation cure process parameters for glass fiber reinforced polymer composites,” Mater Des, vol. 32, no. 3, pp. 1129–1137, 2011, doi: https://doi.org/10.1016/j.matdes.2010.11.001. [39] A. Sheel Wali, S. Kumar, and D. Khan, “A review on recent development and application of radiation curing,” Mater Today Proc, vol. 82, pp. 68–74, 2023, doi: https://doi.org/10.1016/j.matpr.2022.11.342. [40] A. G. Thite, K. Krishnanand, and P. K. Panda, “Recent Advances in Electron Beam Processing of Textile Materials,” in Applications of High Energy Radiations: Synthesis and Processing of Polymeric Materials, S. R. Chowdhury, Ed., Singapore: Springer Nature Singapore, 2023, pp. 457–494. doi: 10.1007/978-981-19-9048-9_14. [41] M. R. Cleland and F. Stichelbaut, “Radiation processing with high-energy X-rays,” Radiation Physics and Chemistry, vol. 84, pp. 91–99, 2013, doi: https://doi.org/10.1016/j.radphyschem.2012.06.038. [42] A. Endruweit, M. S. Johnson, and A. C. Long, “Curing of composite components by ultraviolet radiation: A review,” Apr. 2006. doi: 10.1002/pc.20166. 94 [43] S. K. Ghosh and N. C. Das, “Application of Radiation Curing on Properties and Performance of Polymers and Polymer Composites,” in Applications of High Energy Radiations: Synthesis and Processing of Polymeric Materials, S. R. Chowdhury, Ed., Singapore: Springer Nature Singapore, 2023, pp. 1–39. doi: 10.1007/978-981-19-9048-9_1. [44] C. Decker, “Kinetic study and new applications of UV radiation curing,” Macromol Rapid Commun, vol. 23, no. 18, pp. 1067–1093, Dec. 2002, doi: 10.1002/marc.200290014. [45] I. Calvez, S. Davoudi, C. R. Szczepanski, and V. Landry, “Low-gloss UV-curable coatings: Light mechanisms, formulations and processes — A review,” Prog Org Coat, vol. 171, p. 107039, 2022, doi: https://doi.org/10.1016/j.porgcoat.2022.107039. [46] R. B. M. Atif and J. Yang, “Cationically UV-Cured Epoxy Composites,” Polymer Reviews, vol. 55, no. 1, pp. 90–106, 2015, doi: 10.1080/15583724.2014.963236. [47] Y.-J. Park, D.-H. Lim, H.-J. Kim, D.-S. Park, and I.-K. Sung, “UV- and thermal-curing behaviors of dual-curable adhesives based on epoxy acrylate oligomers,” Int J Adhes Adhes, vol. 29, no. 7, pp. 710–717, 2009, doi: https://doi.org/10.1016/j.ijadhadh.2009.02.001. [48] M. Nachtane et al., “An Overview of the Recent Advances in Composite Materials and Artificial Intelligence for Hydrogen Storage Vessels Design,” Journal of Composites Science, vol. 7, no. 3, 2023, doi: 10.3390/jcs7030119. [49] C. J. Ogugua et al., “Energy analysis of autoclave CFRP manufacturing using thermodynamics based models,” Compos Part A Appl Sci Manuf, vol. 166, p. 107365, 2023, doi: https://doi.org/10.1016/j.compositesa.2022.107365. 95 [50] J. Tatar and S. Milev, “Durability of Externally Bonded Fiber-Reinforced Polymer Composites in Concrete Structures: A Critical Review,” Polymers (Basel), vol. 13, no. 5, 2021, doi: 10.3390/polym13050765. [51] C. Chandrasekaran, “8 - Curing Technology,” in Anticorrosive Rubber Lining, C. Chandrasekaran, Ed., in Plastics Design Library. , William Andrew Publishing, 2017, pp. 57–65. doi: https://doi.org/10.1016/B978-0-323-44371-5.00008-6. [52] I. R. Chowdhury and J. Summerscales, “Cool-Clave—An Energy Efficient Autoclave,” Journal of Composites Science, vol. 7, no. 2, 2023, doi: 10.3390/jcs7020082. [53] H. Li, E. A. Evans, and G.-X. Wang, “Determination of surface heating condition for a desired thermal growth environment in an industry-size hydrothermal autoclave,” in Heat Transfer Summer Conference, 2003, pp. 295–302. [54] J. Ramaswamy Setty, A. R. Upadhya, G. N. Dayananda, G. M. Kamalakannan, and J. Christopher Daniel, “Autoclaves for aerospace applications: Issues and challenges,” International Journal of Aerospace Engineering, 2011, doi: 10.1155/2011/985871. [55] A. A. Johnston, “An integrated model of the development of process-induced deformation in autoclave processing of composite structures,” 1997. [Online]. Available: https://open.library.ubc.ca/collections/831/items/1.0088805 [56] K. Balasubramanian, M. T. H. Sultan, and N. Rajeswari, “4 - Manufacturing techniques of composites for aerospace applications,” in Sustainable Composites for Aerospace Applications, M. Jawaid and M. Thariq, Eds., in Woodhead Publishing Series in Composites Science and Engineering. , Woodhead Publishing, 2018, pp. 55–67. doi: https://doi.org/10.1016/B978-0-08-102131-6.00004-9. 96 [57] M. A. Yalcinkaya, E. M. Sozer, and M. C. Altan, “Effect of external pressure and resin flushing on reduction of process-induced voids and enhancement of laminate quality in heated-VARTM,” Compos Part A Appl Sci Manuf, vol. 121, pp. 353–364, 2019, doi: https://doi.org/10.1016/j.compositesa.2019.03.040. [58] L. Lu et al., “Three-Dimensional Thermo–Chemo–Mechanical Coupled Curing Analysis for the Filament Wound Composite Shell,” Polymers (Basel), vol. 16, no. 12, 2024, doi: 10.3390/polym16121643. [59] M. Lombardini, M. Chiesa, A. Scribante, M. Colombo, and C. Poggio, “Influence of polymerization time and depth of cure of resin composites determined by Vickers hardness,” Dent Res J (Isfahan), vol. 9, no. 6, p. 735, 2012. [60] T. A. Weber, J.-C. Arent, L. Münch, M. Duhovic, and J. M. Balvers, “A fast method for the generation of boundary conditions for thermal autoclave simulation,” Compos Part A Appl Sci Manuf, vol. 88, pp. 216–225, 2016, doi: https://doi.org/10.1016/j.compositesa.2016.05.036. [61] T. S. Mesogitis, A. A. Skordos, and A. C. Long, “Uncertainty in the manufacturing of fibrous thermosetting composites: A review,” Compos Part A Appl Sci Manuf, vol. 57, pp. 67–75, 2014, doi: https://doi.org/10.1016/j.compositesa.2013.11.004. [62] D. Dolkun, H. Wang, H. Wang, and Y. Ke, “An efficient thermal cure profile design method for autoclave curing of large size mold,” The International Journal of Advanced Manufacturing Technology, vol. 114, no. 7, pp. 2499–2514, 2021, doi: 10.1007/s00170-021-07015-4. 97 [63] N. Slesinger, T. Shimizu, A. R. A. Arafath, and A. Poursartip, “HEAT TRANSFER COEFFICIENT DISTRIBUTION INSIDE AN AUTOCLAVE.” [64] A. Fisher, A. Levy, and J. Kratz, “Effects of heat transfer coefficient variations on composite curing,” J Compos Mater, vol. 57, no. 3, pp. 363–376, Feb. 2023, doi: 10.1177/00219983221145506. [65] J. Jan, E. Prabhu, X. Chen, and U. Weiss, “Enhanced Heat Transfer Coefficient (HTC) Method to Model Air Quench Process: HTC Patching for More Accurate FEA Temperature Calculation,” SAE International Journal of Passenger Cars - Mechanical Systems, vol. 9, no. 2, pp. 489–496, Apr. 2016, doi: 10.4271/2016-01-1383. [66] T. Bohne, T. Frerich, J. Jendrny, J.-P. Jürgens, and V. Ploshikhin, “Simulation and validation of air flow and heat transfer in an autoclave process for definition of thermal boundary conditions during curing of composite parts,” J Compos Mater, vol. 52, no. 12, pp. 1677–1687, 2018, doi: 10.1177/0021998317729210. [67] M. Ramezankhani, B. Crawford, A. Narayan, H. Voggenreiter, R. Seethaler, and A. S. Milani, “Making costly manufacturing smart with transfer learning under limited data: A case study on composites autoclave processing,” J Manuf Syst, vol. 59, pp. 345–354, 2021, doi: https://doi.org/10.1016/j.jmsy.2021.02.015. [68] M. Müller-Pabel, J. A. Rodríguez Agudo, and M. Gude, “Measuring and understanding cure-dependent viscoelastic properties of epoxy resin: A review,” Polym Test, vol. 114, p. 107701, 2022, doi: https://doi.org/10.1016/j.polymertesting.2022.107701. [69] H. Madhav, N. Singh, and G. Jaiswar, “Chapter 4 - Thermoset, bioactive, metal–polymer composites for medical applications,” in Materials for Biomedical Engineering, V. 98 Grumezescu and A. M. Grumezescu, Eds., Elsevier, 2019, pp. 105–143. doi: https://doi.org/10.1016/B978-0-12-816874-5.00004-9. [70] B. Tao, Z. Yin, and Y. Xiong, “ACF curing process optimization based on degree of cure considering contact resistance degradation of joints,” Soldering & Surface Mount Technology, vol. 22, no. 4, pp. 4–12, Sep. 2010, doi: 10.1108/09540911011076835. [71] S. Amini Niaki, E. Haghighat, T. Campbell, A. Poursartip, and R. Vaziri, “Physics-informed neural network for modelling the thermochemical curing process of composite-tool systems during manufacture,” Comput Methods Appl Mech Eng, vol. 384, Oct. 2021, doi: 10.1016/j.cma.2021.113959. [72] R. Hardis, J. L. P. Jessop, F. E. Peters, and M. R. Kessler, “Cure kinetics characterization and monitoring of an epoxy resin using DSC, Raman spectroscopy, and DEA,” Compos Part A Appl Sci Manuf, vol. 49, pp. 100–108, 2013, doi: https://doi.org/10.1016/j.compositesa.2013.01.021. [73] Y. Gao et al., “Optimization strategy for curing ultra-thick composite laminates based on multi-objective genetic algorithm,” Composites Communications, vol. 31, p. 101115, 2022, doi: https://doi.org/10.1016/j.coco.2022.101115. [74] X. Hui, Y. Xu, W. Zhang, and W. Zhang, “Cure process evaluation of CFRP composites via neural network: From cure kinetics to thermochemical coupling,” Compos Struct, vol. 288, p. 115341, 2022, doi: https://doi.org/10.1016/j.compstruct.2022.115341. [75] D. Wackerly, W. Mendenhall, and R. L. Scheaffer, Mathematical Statistics with Applications, SEVENTH EDITION. Thomson Learning, Inc., 2008. 99 [76] H. Abdi, “Coefficient of variation,” Encyclopedia of research design, vol. 1, no. 5, pp. 169–171, 2010. [77] X. Wang, Q. Wang, L. Gao, and Y. Jia, “Effect of heat treatment on curing uniformity of fiber composite laminates,” Polymers and Polymer Composites, vol. 25, no. 1, pp. 29–34, 2017. [78] Y. Lin and Z. Guan, “The Use of Machine Learning for the Prediction of the Uniformity of the Degree of Cure of a Composite in an Autoclave,” Aerospace, vol. 8, no. 5, 2021, doi: 10.3390/aerospace8050130. [79] J. Zhu, T. Frerich, and A. S. Herrmann, “CFD modeling and validation of heat transfer inside an autoclave based on a mesh independency study,” J Compos Mater, vol. 55, no. 18, pp. 2469–2487, 2021, doi: 10.1177/0021998320979043. [80] M. Szarski and S. Chauhan, “Composite temperature profile and tooling optimization via Deep Reinforcement Learning,” Compos Part A Appl Sci Manuf, vol. 142, p. 106235, 2021, doi: https://doi.org/10.1016/j.compositesa.2020.106235. [81] W. Tang, Y. Xu, X. Hui, and W. Zhang, “Multi-Objective Optimization of Curing Profile for Autoclave Processed Composites: Simultaneous Control of Curing Time and Process-Induced Defects,” Polymers (Basel), vol. 14, no. 14, Jul. 2022, doi: 10.3390/polym14142815. [82] C. Duffner, “Experimental study of the pre-gelation behaviour of composite prepreg,” 2019. [Online]. Available: https://open.library.ubc.ca/collections/24/items/1.0387323 100 [83] P. Hubert, “9 - Autoclave and out-of-autoclave processing of prepregs,” in Design and Manufacture of Structural Composites, L. Harper and M. Clifford, Eds., in Woodhead Publishing Series in Composites Science and Engineering. , Woodhead Publishing, 2023, pp. 209–236. doi: https://doi.org/10.1016/B978-0-12-819160-6.00020-2. [84] H. Yu et al., “The Effect of Shear Rate on Dynamic Gelation of Phenol Formaldehyde Resin Gel in Porous Media,” Gels, vol. 8, no. 3, 2022, doi: 10.3390/gels8030185. [85] A. Dong, Y. Zhao, X. Zhao, and Q. Yu, “Cure Cycle Optimization of Rapidly Cured Out-Of-Autoclave Composites,” Materials, vol. 11, no. 3, 2018, doi: 10.3390/ma11030421. [86] I. H. Sarker, “Machine Learning: Algorithms, Real-World Applications and Research Directions,” SN Comput Sci, vol. 2, no. 3, p. 160, 2021, doi: 10.1007/s42979-021-00592-x. [87] M. Elahi, S. O. Afolaranmi, J. L. Martinez Lastra, and J. A. Perez Garcia, “A comprehensive literature review of the applications of AI techniques through the lifecycle of industrial equipment,” Discover Artificial Intelligence, vol. 3, no. 1, p. 43, 2023, doi: 10.1007/s44163-023-00089-x. [88] S. Rezvan, M. J. Moradi, H. Dabiri, K. Daneshvar, M. Karakouzian, and V. Farhangi, “Application of Machine Learning to Predict the Mechanical Characteristics of Concrete Containing Recycled Plastic-Based Materials,” Applied Sciences, vol. 13, no. 4, 2023, doi: 10.3390/app13042033. [89] N. Ganasen, L. Krishnaraj, A. Bahrami, H. Almujibah, H. Panchal, and M. Abdul Zahra, “Machine learning application to predict the mechanical properties of glass fiber mortar,” vol. 180, p. 103454, Jul. 2023, doi: 10.1016/j.advengsoft.2023.103454. 101 [90] M. Javaid, A. Haleem, R. P. Singh, S. Rab, and R. Suman, “Exploring impact and features of machine vision for progressive industry 4.0 culture,” Sensors International, vol. 3, p. 100132, 2022, doi: https://doi.org/10.1016/j.sintl.2021.100132. [91] K. Palanikumar, E. Natarajan, and A. Ponshanmugakumar, “Chapter 6 - Application of machine vision technology in manufacturing industries—a study,” in Machine Intelligence in Mechanical Engineering, K. Palanikumar, E. Natarajan, S. Ramesh, and J. P. Davim, Eds., in Woodhead Publishing Reviews: Mechanical Engineering Series. , Academic Press, 2024, pp. 91–122. doi: https://doi.org/10.1016/B978-0-443-18644-8.00018-6. [92] E. Ropelewska and K. P. Rutkowski, “The Classification of Peaches at Different Ripening Stages Using Machine Learning Models Based on Texture Parameters of Flesh Images,” Agriculture, vol. 13, no. 2, 2023, doi: 10.3390/agriculture13020498. [93] K. Prakash and S. Saradha, “Efficient prediction and classification for cirrhosis disease using LBP, GLCM and SVM from MRI images,” Mater Today Proc, vol. 81, pp. 383–388, 2023, doi: https://doi.org/10.1016/j.matpr.2021.03.418. [94] Y. T. Acquaah, B. Gokaraju, R. C. Tesiero, and G. H. Monty, “Thermal Imagery Feature Extraction Techniques and the Effects on Machine Learning Models for Smart HVAC Efficiency in Building Energy,” Remote Sens (Basel), vol. 13, no. 19, 2021, doi: 10.3390/rs13193847. [95] G. L. Anderson and D. J. Macon, “5 - Properties of adhesives,” in Adhesive Bonding (Second Edition), Second Edition., R. D. Adams, Ed., in Woodhead Publishing Series in Welding and Other Joining Technologies. , Woodhead Publishing, 2021, pp. 133–155. doi: https://doi.org/10.1016/B978-0-12-819954-1.00002-2. 102 [96] Hexcel Corporation, “HexPly® 8552 Product Data,” Feb. 2013. [97] Hexcel Corporation, “HexTow® IM7 Carbon Fiber Product Data Sheet,” 2016, Hexcel Composites Publication. [98] M. Yakout and M. A. Elbestawi, “Insights on laser additive manufacturing of invar 36,” in Additive Manufacturing Applications for Metals and Composites, IGI Global, 2020, pp. 71–93. [99] M. K. Bekir Akgul and F. Erden, “The puzzling thermal expansion behavior of invar alloys: a review on process-structure-property relationship,” Critical Reviews in Solid State and Materials Sciences, vol. 49, no. 2, pp. 254–307, 2024, doi: 10.1080/10408436.2023.2170975. [100] “RAVEN Simulation Software | Convergent Version 3.15.3,” Convergent Manufacturing Technologies Inc. Accessed: Jul. 23, 2024. [Online]. Available: https://www.convergent.ca/products/raven-simulation-software [101] S. ’Ling, W. ’Moebs, and J. ’Sanny, UNIVERSITY PHYSICS II: THERMODYNAMICS, ELECTRICITY, & MAGNETISM, vol. 2. OpenStax, 2016. [102] M. Hall, X. Zeng, T. Shelley, and P. Schubel, “Dielectric parameter independent curing analysis of out-of-autoclave carbon fibre/epoxy composites,” Compos Part A Appl Sci Manuf, vol. 175, p. 107755, 2023, doi: https://doi.org/10.1016/j.compositesa.2023.107755. 103 [103] M. Hall, X. Zeng, T. Shelley, and P. Schubel, “In Situ Thermoset Cure Sensing: A Review of Correlation Methods,” Polymers (Basel), vol. 14, no. 15, 2022, doi: 10.3390/polym14152978. [104] S. Fahimi, “Towards a multiscale viscoelastic flow-stress model for composite processing,” 2023. [Online]. Available: https://open.library.ubc.ca/collections/24/items/1.0435205 [105] M. Ramezankhani, “Data-efficient and uncertainty-aware hybrid machine learning in advanced composites manufacturing,” 2023. [Online]. Available: https://open.library.ubc.ca/collections/24/items/1.0432643 [106] A. Munk, “Accelerating Bayesian inference in probabilistic programming,” 2023. [Online]. Available: https://open.library.ubc.ca/collections/24/items/1.0435206 [107] H. Monod, C. Naud, and D. Makowski, “Uncertainty and sensitivity analysis for crop models,” Working with dynamic crop models: Evaluation, analysis, parameterization, and applications, vol. 4, pp. 55–100, 2006. [108] K. K. Benke, K. E. Lowell, and A. J. Hamilton, “Parameter uncertainty, sensitivity analysis and prediction error in a water-balance hydrological model,” Math Comput Model, vol. 47, no. 11, pp. 1134–1149, 2008, doi: https://doi.org/10.1016/j.mcm.2007.05.017. [109] A. Konak, D. W. Coit, and A. E. Smith, “Multi-objective optimization using genetic algorithms: A tutorial,” Reliab Eng Syst Saf, vol. 91, no. 9, pp. 992–1007, 2006, doi: https://doi.org/10.1016/j.ress.2005.11.018. 104 [110] P. Bajpai and M. Kumar, “Genetic algorithm–an approach to solve global optimization problems,” Indian Journal of computer science and engineering, vol. 1, no. 3, pp. 199–206, 2010. [111] M. A. Albadr, S. Tiun, M. Ayob, and F. AL-Dhief, “Genetic Algorithm Based on Natural Selection Theory for Optimization Problems,” Symmetry (Basel), vol. 12, no. 11, 2020, doi: 10.3390/sym12111758. [112] V. S. Summanwar, V. K. Jayaraman, B. D. Kulkarni, H. S. Kusumakar, K. Gupta, and J. Rajesh, “Solution of constrained optimization problems by multi-objective genetic algorithm,” Comput Chem Eng, vol. 26, no. 10, pp. 1481–1492, 2002, doi: https://doi.org/10.1016/S0098-1354(02)00125-4. [113] C. Xun and P. Wu, “A Generic Multi-Objective Optimization of Machining Processes Using an End-to-End Evolutionary Algorithm,” Machines, vol. 12, no. 9, 2024, doi: 10.3390/machines12090635. [114] R. M. Haralick, K. Shanmugam, and I. Dinstein, “Textural Features for Image Classification,” IEEE Trans Syst Man Cybern, vol. SMC-3, no. 6, pp. 610–621, 1973, doi: 10.1109/TSMC.1973.4309314. [115] H. Wei et al., “A texture feature extraction method considering spatial continuity and gray diversity,” International Journal of Applied Earth Observation and Geoinformation, vol. 130, p. 103896, 2024, doi: https://doi.org/10.1016/j.jag.2024.103896. [116] L.-K. Chang et al., “Machine learning applied to property prediction of metal additive manufacturing products with textural features extraction,” The International Journal of 105 Advanced Manufacturing Technology, vol. 132, no. 1, pp. 83–98, 2024, doi: 10.1007/s00170-024-13165-y. [117] N. Zayed and H. A. Elnemr, “Statistical Analysis of Haralick Texture Features to Discriminate Lung Abnormalities,” Int J Biomed Imaging, vol. 2015, 2015, doi: 10.1155/2015/267807. [118] I. Vrbik et al., “Haralick texture feature analysis for quantifying radiation response heterogeneity in murine models observed using Raman spectroscopic mapping,” PLoS One, vol. 14, no. 2, p. e0212225, 2019. [119] F. Mendoza and R. Lu, “Basics of image analysis,” Hyperspectral imaging technology in food and agriculture, pp. 9–56, 2015. [120] T. Qamar and N. Z. Bawany, “Understanding the black-box: towards interpretable and reliable deep learning models,” PeerJ Comput Sci, vol. 9, p. e1629, 2023, doi: 10.7717/peerj-cs.1629. [121] V. Buhrmester, D. Münch, and M. Arens, “Analysis of Explainers of Black Box Deep Neural Networks for Computer Vision: A Survey,” Mach Learn Knowl Extr, vol. 3, no. 4, pp. 966–989, 2021, doi: 10.3390/make3040048. [122] A. Humeau-Heurtier, “Texture Feature Extraction Methods: A Survey,” IEEE Access, vol. 7, pp. 8975–9000, 2019, doi: 10.1109/ACCESS.2018.2890743. [123] S. Sulochana and R. Vidhya, “Texture based image retrieval using framelet transform-gray level co-occurrence matrix (GLCM),” International Journal of Advanced Research in Artificial Intelligence, vol. 2, no. 2, pp. 68–73, 2013. 106 [124] A. Ramola, A. K. Shakya, and D. Van Pham, “Study of statistical methods for texture analysis and their modern evolutions,” Engineering Reports, vol. 2, no. 4, p. e12149, 2020. [125] T. Löfstedt, P. Brynolfsson, T. Asklund, T. Nyholm, and A. Garpebring, “Gray-level invariant Haralick texture features,” PLoS One, vol. 14, no. 2, p. e0212110, 2019. [126] C. E. Honeycutt and R. Plotnick, “Image analysis techniques and gray-level co-occurrence matrices (GLCM) for calculating bioturbation indices and characterizing biogenic sedimentary structures,” Comput Geosci, vol. 34, no. 11, pp. 1461–1472, 2008. [127] Y. Park and J.-M. Guldmann, “Measuring continuous landscape patterns with Gray-Level Co-Occurrence Matrix (GLCM) indices: An alternative to patch metrics?,” Ecol Indic, vol. 109, p. 105802, 2020. [128] D. L. Campbell, H. Kang, and S. Shokouhi, “Application of Haralick texture features in brain [18F]-florbetapir positron emission tomography without reference region normalization,” Clin Interv Aging, pp. 2077–2086, 2017. [129] D. Dhall, R. Kaur, and M. Juneja, “Machine Learning: A Review of the Algorithms and Its Applications,” in Proceedings of ICRIC 2019, P. K. Singh, A. K. Kar, Y. Singh, M. H. Kolekar, and S. Tanwar, Eds., Cham: Springer International Publishing, 2020, pp. 47–63. [130] L. Zhou, S. Pan, J. Wang, and A. V Vasilakos, “Machine learning on big data: Opportunities and challenges,” Neurocomputing, vol. 237, pp. 350–361, 2017, doi: https://doi.org/10.1016/j.neucom.2017.01.026. 107 [131] K. J. Bergen, P. A. Johnson, M. V de Hoop, and G. C. Beroza, “Machine learning for data-driven discovery in solid Earth geoscience,” Science (1979), vol. 363, no. 6433, p. eaau0323, 2019. [132] E. F. Morales and H. J. Escalante, “Chapter 6 - A brief introduction to supervised, unsupervised, and reinforcement learning,” in Biosignal Processing and Classification Using Computational Learning and Intelligence, A. A. Torres-García, C. A. Reyes-García, L. Villaseñor-Pineda, and O. Mendoza-Montoya, Eds., Academic Press, 2022, pp. 111–129. doi: https://doi.org/10.1016/B978-0-12-820125-1.00017-8. [133] V. François-Lavet, P. Henderson, R. Islam, M. G. Bellemare, and J. Pineau, “An introduction to deep reinforcement learning,” Foundations and Trends® in Machine Learning, vol. 11, no. 3–4, pp. 219–354, 2018. [134] R. Verma, V. Nagar, and S. Mahapatra, “Introduction to supervised learning,” Data Analytics in Bioinformatics: A Machine Learning Perspective, pp. 1–34, 2021. [135] S. K. Jain, A. Das, and D. K. Srivastava, “Application of ANN for reservoir inflow prediction and operation,” J Water Resour Plan Manag, vol. 125, no. 5, pp. 263–271, 1999. [136] H. R. Maier et al., “Exploding the myths: An introduction to artificial neural networks for prediction and forecasting,” Environmental Modelling & Software, vol. 167, p. 105776, 2023, doi: https://doi.org/10.1016/j.envsoft.2023.105776. [137] M. Goyal, R. Goyal, P. Venkatappa Reddy, and B. Lall, “Activation Functions,” in Deep Learning: Algorithms and Applications, W. Pedrycz and S.-M. Chen, Eds., Cham: Springer International Publishing, 2020, pp. 1–30. doi: 10.1007/978-3-030-31760-7_1. 108 [138] Y. Bai, “RELU-function and derived function review,” in SHS Web of Conferences, EDP Sciences, 2022, p. 02006. [139] X. Ying, “An Overview of Overfitting and its Solutions,” J Phys Conf Ser, vol. 1168, no. 2, p. 022022, 2019, doi: 10.1088/1742-6596/1168/2/022022. [140] O. A. Montesinos López, A. Montesinos López, and J. Crossa, “Overfitting, Model Tuning, and Evaluation of Prediction Performance,” in Multivariate Statistical Machine Learning Methods for Genomic Prediction, O. A. Montesinos López, A. Montesinos López, and J. Crossa, Eds., Cham: Springer International Publishing, 2022, pp. 109–139. doi: 10.1007/978-3-030-89010-0_4. [141] Z. Reitermanova, “Data splitting,” in WDS, Matfyzpress Prague, 2010, pp. 31–36. [142] I. Ahmed, A. Chehri, and G. Jeon, “Artificial Intelligence and Blockchain Enabled Smart Healthcare System for Monitoring and Detection of COVID-19 in Biomedical Images,” IEEE/ACM Trans Comput Biol Bioinform, pp. 1–10, 2023, doi: 10.1109/TCBB.2023.3294333. [143] T. Chen et al., “A vision transformer machine learning model for COVID-19 diagnosis using chest X-ray images,” Healthcare Analytics, vol. 5, p. 100332, 2024, doi: https://doi.org/10.1016/j.health.2024.100332. [144] “Overfitting and Underfitting.” Accessed: Jul. 31, 2024. [Online]. Available: https://www.kaggle.com/code/ryanholbrook/overfitting-and-underfitting [145] “Using SHAP Values to Explain How Your Machine Learning Model Works | by Vinícius Trevisan | Towards Data Science.” Accessed: Jul. 31, 2024. [Online]. Available: 109 https://towardsdatascience.com/using-shap-values-to-explain-how-your-machine-learning-model-works-732b3f40e137 [146] H. Chen, I. C. Covert, S. M. Lundberg, and S.-I. Lee, “Algorithms to estimate Shapley value feature attributions,” Nat Mach Intell, vol. 5, no. 6, pp. 590–601, 2023, doi: 10.1038/s42256-023-00657-x. [147] D. Chicco, M. J. Warrens, and G. Jurman, “The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation,” PeerJ Comput Sci, vol. 7, p. e623, 2021. [148] M. V. Shcherbakov, A. Brebels, N. L. Shcherbakova, A. P. Tyukov, T. A. Janovsky, and V. A. Kamaev, “A survey of forecast error measures,” World Appl Sci J, vol. 24, no. 24, pp. 171–176, 2013. [149] S. Van der Walt et al., “scikit-image: image processing in Python,” PeerJ, vol. 2, p. e453, 2014. [150] S. Raschka, “Model evaluation, model selection, and algorithm selection in machine learning,” arXiv preprint arXiv:1811.12808, 2018. [151] F. Pedregosa, “Scikit‐learn: Machine learning in python Fabian,” Journal of machine learning research, vol. 12, p. 2825, 2011. [152] A. G. Asuero, A. Sayago, and A. G. González, “The correlation coefficient: An overview,” Crit Rev Anal Chem, vol. 36, no. 1, pp. 41–59, 2006. 110 [153] M. Ginocchi, F. Ponci, and A. Monti, “Sensitivity analysis and power systems: Can we bridge the gap? A review and a guide to getting started,” Energies (Basel), vol. 14, no. 24, p. 8274, 2021. [154] S. Marino, I. B. Hogue, C. J. Ray, and D. E. Kirschner, “A methodology for performing global uncertainty and sensitivity analysis in systems biology,” J Theor Biol, vol. 254, no. 1, pp. 178–196, 2008. [155] “Infrared Camera Accuracy and Uncertainty in Plain Language | Teledyne FLIR.” Accessed: Aug. 25, 2024. [Online]. Available: https://www.flir.ca/discover/rd-science/infrared-camera-accuracy-and-uncertainty-in-plain-language/ [156] H. K. Thakkar, H. Shukla, and P. K. Sahoo, “Chapter 2 - Metaheuristics in classification, clustering, and frequent pattern mining,” in Cognitive Big Data Intelligence with a Metaheuristic Approach, S. Mishra, H. K. Tripathy, P. K. Mallick, A. K. Sangaiah, and G.-S. Chae, Eds., Academic Press, 2022, pp. 21–70. doi: https://doi.org/10.1016/B978-0-323-85117-6.00005-4. [157] S. K. BASU, “CHAPTER 9 - A Cursory Look at Parallel Architectures and Biologically Inspired Computing,” in Soft Computing and Intelligent Systems, N. K. SINHA and M. M. GUPTA, Eds., San Diego: Academic Press, 2000, pp. 185–216. doi: https://doi.org/10.1016/B978-012646490-0/50012-3. [158] M. Ghaderian and F. Veysi, “Multi-objective optimization of energy efficiency and thermal comfort in an existing office building using NSGA-II with fitness approximation: A case study,” Journal of Building Engineering, vol. 41, p. 102440, 2021, doi: https://doi.org/10.1016/j.jobe.2021.102440. 111 [159] C. Zhang and X. Ma, “NSGA-II Algorithm with a Local Search Strategy for Multiobjective Optimal Design of Dry-Type Air-Core Reactor,” Math Probl Eng, vol. 2015, no. 1, p. 839035, Jan. 2015, doi: https://doi.org/10.1155/2015/839035. [160] Y. Zhang, G. Feng, and B. Liu, “Sensitivity analysis and multi-objective optimization strategy of the curing profile for autoclave processed thick composite laminates,” Polymers (Basel), vol. 15, no. 11, p. 2437, 2023. [161] J. Blank and K. Deb, “Pymoo: Multi-Objective Optimization in Python,” IEEE Access, vol. 8, pp. 89497–89509, 2020, doi: 10.1109/ACCESS.2020.2990567. [162] K. Deb and R. B. Agrawal, “Simulated binary crossover for continuous search space,” Complex systems, vol. 9, no. 2, pp. 115–148, 1995. [163] A. Hassanat, K. Almohammadi, E. Alkafaween, E. Abunawas, A. Hammouri, and V. B. S. Prasath, “Choosing Mutation and Crossover Ratios for Genetic Algorithms—A Review with a New Dynamic Approach,” Information, vol. 10, no. 12, 2019, doi: 10.3390/info10120390. [164] M. Madanchian and H. Taherdoost, “A comprehensive guide to the TOPSIS method for multi-criteria decision making,” Sustainable Social Development, vol. 1, Aug. 2023, doi: 10.54517/ssd.v1i1.2220.
Thesis/Dissertation
eng
Mechanical Engineering
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Milani, Abbas S.
A feasibility study on artificial neural network-based prediction and optimization of autoclave curing process outcomes via simulation-based thermal images and Haralick texture features
Text