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
department or school: Applied Science, Faculty of
department or school: Engineering, School of (Okanagan)
data provider: DSpace
degree campus: UBCO
creator: Batouei, Mohammad Amin
issued: 2024-12-09T20:50:24Z
issued: 2024
related degree: Master of Applied Science - MASc
degree grantor: University of British Columbia
description: 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 …
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/89805
note: 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 autoc…
has type: Thesis/Dissertation
date issued: 2025-02
is shown at: 10.14288/1.0447427
language: eng
degree discipline: Mechanical Engineering
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
contributor: Milani, Abbas S.
type: Text
identifier URI: http://hdl.handle.net/2429/89805