Applied Science, Faculty of
Engineering, School of (Okanagan)
DSpace
UBCO
Cosco, Brett Aaron
Master of Applied Science - MASc
University of British Columbia
Schlieren imaging, a technique that allows for density variations in transparent mediums to be visualized, was brought to prominence in 1864 and is still in use today. This technique has been used to study supersonic motion in aerospace applications, as well as visualizing airflow in speech. Despite the diversity in application and reference to its use as a leak localization tool, there is no literature supporting its use in composite manufacturing vacuum bags. The purpose of this research is twofold. First, a Schlieren system is developed. Second, a novel method of leakage localization in vacuum bags premised on the use of Schlieren imaging is hypothesizes and the results of the experimentation are presented as proof of concept. An introduction to vacuum bag based composite manufacturing and the importance of leakage localization is provided. The standard methods of leakage detection and localization are discussed and analyzed. The analysis culminates in the identification of the need for a noninvasive, quick, and definitive method of leakage localization. Research around methods of detection satisfying these criteria led to the hypothesis that Schlieren imaging is capable of visualizing flow into a leakage allowing for definitive localization. An analysis of the data recorded during the experiments demonstrates the ability for Schlieren imaging to visualize flow entering a vacuum bag at a leak.
A NOVEL APPLICATION OF SCHLIEREN IMAGING: LEAKAGE LOCALIZATION IN COMPOSITE MANUFACTURING VACUUM BAGS by Brett Aaron Cosco B.A.Sc., University of British Columbia, 2020 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) April 2023 © Brett Aaron Cosco, 2023 ii The following individuals certify that they have read, and recommend to the College of Graduate Studies for acceptance, a thesis entitled: A NOVEL APPLICATION OF SCHLIEREN IMAGING: LEAK LOCALIZATION IN COMPOSITE MANUFACTURING VACUUM BAGS submitted by Brett Aaron Cosco in partial fulfillment of the requirements of the degree of Master of Applied Science . Homayoun Najjaran, University of Victoria Supervisor Sumi Siddiqua, School of Engineering Co-Supervisor Abbas Milani, School of Engineering Supervisory Committee Member Jian Liu, School of Engineering Supervisory Committee Member Thomas Johnson, School of Engineering University Examiner iii Abstract Schlieren imaging, a technique that allows for density variations in transparent mediums to be visualized, was brought to prominence in 1864 and is still in use today. This technique has been used to study supersonic motion in aerospace applications, as well as visualizing airflow in speech. Despite the diversity in application and reference to its use as a leak localization tool, there is no literature supporting its use in composite manufacturing vacuum bags. The purpose of this research is twofold. First, a Schlieren system is developed. Second, a novel method of leakage localization in vacuum bags premised on the use of Schlieren imaging is hypothesizes and the results of the experimentation are presented as proof of concept. An introduction to vacuum bag based composite manufacturing and the importance of leakage localization is provided. The standard methods of leakage detection and localization are discussed and analyzed. The analysis culminates in the identification of the need for a noninvasive, quick, and definitive method of leakage localization. Research around methods of detection satisfying these criteria led to the hypothesis that Schlieren imaging is capable of visualizing flow into a leakage allowing for definitive localization. An analysis of the data recorded during the experiments demonstrates the ability for Schlieren imaging to visualize flow entering a vacuum bag at a leak. iv Lay Summary The objective of this thesis is to develop a system capable of visualizing air flowing into a composite manufacturing vacuum bag so that a definitive location of the leak can be determined. The system components can be aligned to leverage physics and optics principles to visualize changes in density. The system, called a Schlieren imaging system, allows for the visualization of flow in transparent mediums such as air. These systems are presently used in industry, however they have yet to be applied to leak localization in vacuum bags. The process of building the system and the experimental procedures are outlined. The thesis discusses the images recorded using the system and provides evidence in support of the use of Schlieren imaging for leak detection in vacuum bags. The main contribution of this research is a novel localization technique capable of definitively locating leakages in vacuum bags. v Preface The leak localization framework presented in Section 2.3.5 is based on work in “Intelligent Vacuum Bagging Leakage Location Prediction” and “Vacuum Bag Leak Detection for Resin Infusion: An Electric Current Based Analogy”. The latter article is published in The International Journal of Advanced Manufacturing Technology, while the former has been submitted to Engineering Applications of Artificial Intelligence for review. vi Table of Contents Abstract ........................................................................................................................... iii Lay Summary ..................................................................................................................iv Preface ............................................................................................................................ v Table of Contents ............................................................................................................vi List of Figures ..................................................................................................................ix List of Tables ..................................................................................................................xv List of Abbreviations ...................................................................................................... xvi Acknowledgements ...................................................................................................... xvii Dedication ................................................................................................................... xviii 1 Introduction ............................................................................................................... 1 1.1 Motivation ........................................................................................................... 3 1.2 Objective ............................................................................................................ 3 1.3 Outline ................................................................................................................ 3 2 Review of Literature .................................................................................................. 5 2.1 Vacuum Bag Processes and the Significance of Leakages ............................... 5 2.2 Methods of Detection ......................................................................................... 7 2.2.1 Pressure Rise .............................................................................................. 7 2.2.2 Ultra Sniffer Test .......................................................................................... 8 2.2.3 Ultrasonic Microphone ................................................................................. 8 vii 2.2.4 Flowrate for Detection.................................................................................. 9 2.3 Methods of Localization ................................................................................... 10 2.3.1 Infrared Thermography .............................................................................. 10 2.3.2 Piezoelectric for Localization ..................................................................... 11 2.3.3 Leak Detection Film and Dye ..................................................................... 12 2.3.4 Flowrate for Localization ............................................................................ 12 2.3.5 Machine Learning ...................................................................................... 14 2.3.5.1 Simulation ........................................................................................... 15 2.3.5.2 Training ............................................................................................... 19 2.4 Schlieren .......................................................................................................... 28 3 Methodology ........................................................................................................... 33 3.1 Experimental Setup .......................................................................................... 33 3.1.1 Converging Optical Element ...................................................................... 34 3.1.2 Light Source .............................................................................................. 34 3.1.3 Light Block ................................................................................................. 35 3.1.4 Imaging Device .......................................................................................... 35 3.2 Iterations of the System ................................................................................... 35 3.3 Experimentation ............................................................................................... 47 3.3.1 Alignment ................................................................................................... 50 3.3.2 Data Collection .......................................................................................... 54 viii 4 Results .................................................................................................................... 56 4.1 No Leakage ...................................................................................................... 58 4.2 Needle in Place ................................................................................................ 73 4.3 The Leakage Experiment ................................................................................. 93 4.4 Analysis .......................................................................................................... 113 5 Conclusions and Recommendations .................................................................... 125 5.1 Limitations of the Research ............................................................................ 126 5.2 Conclusions.................................................................................................... 129 5.3 Future Work ................................................................................................... 130 References: ................................................................................................................. 133 ix List of Figures Figure 1: Basic vacuum bag assembly [4]. ...................................................................... 6 Figure 2: Resistor network which models breather. ...................................................... 18 Figure 3: Ammeters and grounds which represent vacuum ports. ................................ 18 Figure 4: General network architecture trained for the problem. ................................... 25 Figure 5: First iteration of Schlieren system using a converging lens and close up of the lens. © Brett Cosco CC BY-NC-ND 4.0. ........................................................................ 36 Figure 6: Second iteration of the system using concave mirror. © Brett Cosco CC BY-NC-ND 4.0. .................................................................................................................... 37 Figure 7: First Schlieren like images captured with the system. .................................... 40 Figure 8: Thermal plume of candle projected onto screen. ........................................... 40 Figure 9: Frame by frame sequence showing thermal plume. ....................................... 40 Figure 10: Examples of poor system alignment. ........................................................... 41 Figure 11: Example of thermal plume of candle under good system alignment. ........... 41 Figure 12: Images captured with the 1 J1 by system that has been mildly misaligned. 42 Figure 13: Images captured with the 1 J1 when the system is properly aligned. ........... 42 Figure 14: Sample of 1 J1 image quality when cropped to candle flame....................... 43 Figure 15: Sample of D7100 image quality when cropped to candle flame. .................. 43 Figure 16: Mounting platform for camera and light source. © Brett Cosco CC BY-NC-ND 4.0. ................................................................................................................................ 45 Figure 17: Schematic showing side view of the system. © Brett Cosco CC BY-NC-ND 4.0. ................................................................................................................................ 46 x Figure 18: Schematic showing the top view of the system. © Brett Cosco CC BY-NC-ND 4.0. ................................................................................................................................ 46 Figure 19: Schematic showing three-dimensional view of system. © Brett Cosco CC BY-NC-ND 4.0. .................................................................................................................... 47 Figure 20: Uncropped Schlieren Image taken with a smartphone camera. ................... 57 Figure 21: Uncropped Schlieren Image taken with a Nikon 1 J1. .................................. 57 Figure 22: Uncropped Schlieren Image taken with a Nikon D7100. .............................. 58 Figure 23: Frame by frame sequence showing alcohol vapor being introduced to the test area by squeezing the bottle during the no leakage test. .............................................. 60 Figure 24: Frame by frame sequence of alcohol vapor flowing from an open container during the no leakage test. ............................................................................................ 61 Figure 25: Frame by frame sequence showing butane gas from a lighter being introduced to the test area during the no leakage test................................................... 62 Figure 26: Frame by frame sequence showing the thermal plume of a candle centered in the test area during the no leakage test. ................................................................... 63 Figure 27: Frame by frame sequence showing the thermal plume of a candle on the right side of the test area during the no leakage test. .................................................... 64 Figure 28: Frame by frame sequence showing the thermal plume of a candle on the left side of the test area during the no leakage test. ............................................................ 65 Figure 29: Frame by frame sequence showing CO2 being released into the test area from a balloon during the no leakage test. .................................................................... 67 Figure 30: Frame by frame sequence showing CO2 being released into the test area from a compressed gas cylinder during the no leakage test. ........................................ 68 xi Figure 31: Frame by frame sequence showing a burning candle being extinguished during the no leakage test. ............................................................................................ 69 Figure 32: Frame by frame sequence showing the flow of air warmed by the residual heat from an extinguished candle during the no leakage test. ...................................... 71 Figure 33: Frame by frame sequence showing Isobutane being released into the test area from a compressed gas cylinder during the no leakage test. ................................ 72 Figure 34: Frame by frame sequence showing alcohol vapor from the bottle being drawn into the needle during the needle in place test. .................................................. 74 Figure 35: Frame by frame sequence showing alcohol vapor flowing from an open container into the needle during the needle in place test. ............................................. 75 Figure 36: Frame by frame sequence showing butane gas from a lighter being drawn into the needle during the needle in place test. ............................................................. 77 Figure 37: Frame by frame sequence showing air warmed by heat from a hand being drawn into the needle during the needle in place test. .................................................. 79 Figure 38: Frame by frame sequence showing the thermal plume of a burning candle being drawn into the needle during the needle in place test. ......................................... 81 Figure 39: Frame by frame sequence showing CO2 being released from a balloon and drawn into the needle during the needle in place test. .................................................. 83 Figure 40: Frame by frame sequence showing CO2 being poured from an open container and drawn into the needle during the needle in place test. ............................ 85 Figure 41: Frame by frame sequence showing CO2 being released from a compressed gas cylinder and then be drawn into the needle during the needle in place test. .......... 87 xii Figure 42: Frame by frame sequence showing CO2 being released from a compressed gas cylinder above the test area during the needle in place test. .................................. 89 Figure 43: Frame by frame sequence showing Isobutane being released from a compressed gas cylinder during the needle in place test. ............................................. 90 Figure 44: Frame by frame sequence showing the air warmed by the residual heat from an extinguished candle being drawn into the needle during the needle in place test. ... 92 Figure 45: Frame by frame sequence showing alcohol vapor from the bottle being introduced to the test area during the leakage (needle removed) test. ......................... 94 Figure 46: Frame by frame sequence showing alcohol vapor flowing from an open container into the leakage during the leakage (needle removed) test. .......................... 96 Figure 47: Frame by frame sequence showing butane gas from a lighter being drawn into the leakage during the leakage (needle removed) test. .......................................... 97 Figure 48: Frame by frame sequence showing the thermal plume of a burning candle placed to the left of the leakage being drawn into the leakage during the leakage (needle removed) test. .................................................................................................. 99 Figure 49: Frame by frame sequence showing the thermal plume of a burning candle placed to the right of the leakage being drawn into the leakage during the leakage (needle removed) test. ................................................................................................ 100 Figure 50: Frame by frame sequence showing the air warmed by the residual heat from an extinguished candle being drawn into the leakage during the leakage (needle removed) test. ............................................................................................................. 102 Figure 51: Frame by frame sequence showing CO2 being released from a balloon and drawn into the leakage during the leakage (needle removed) test. ............................. 104 xiii Figure 52: Frame by frame sequence showing CO2 being released from a balloon above the test area during the leakage (needle removed) test. .................................. 106 Figure 53: Frame by frame sequence showing CO2 being released from a compressed gas cylinder during the leakage (needle removed) test. .............................................. 107 Figure 54: Frame by frame sequence showing CO2 being poured from an open container and drawn into the leakage during the leakage (needle removed) test. ...... 109 Figure 55: Frame by frame sequence showing CO2 being poured from an open container above the test area and drawn into the leakage during the leakage (needle removed) test. ............................................................................................................. 111 Figure 56: Frame by frame sequence showing Isobutane being released from a compressed gas cylinder and drawn into the leakage during the leakage (needle removed) test. ............................................................................................................. 113 Figure 57: Cropped image of alcohol vapor being pulled into the needle. ................... 114 Figure 58: Cropped frame by frame sequence demonstrating butane flowing into the needle. ........................................................................................................................ 114 Figure 59: Cropped image illustrating the flow caused by a leakage using body heat as the density modifier. .................................................................................................... 115 Figure 60: Cropped image showing the thermal plume of a candle being drawn into needle. ........................................................................................................................ 115 Figure 61: Cropped image of compressed CO2 entering the needle. .......................... 116 Figure 62: Cropped image showing CO2 released from a balloon being drawn into the needle (mirror defects that were used as reference points circled in red). .................. 116 Figure 63: Cropped image showing faint flow of CO2 into the needle. ........................ 117 xiv Figure 64: Cropped image showing isobutane flowing into the needle. ...................... 117 Figure 65: Cropped image of air, warmed by the residual heat of a candle, being pulled into the needle (mirror defects circled in red). ............................................................. 118 Figure 66: Cropped image showing alcohol vapor being drawn into the leakage. ....... 118 Figure 67: Cropped frame by frame sequence showing butane being drawn into the leakage. ....................................................................................................................... 119 Figure 68: Cropped image showing the thermal plume of a candle being pulled into the leak. ............................................................................................................................. 120 Figure 69: Cropped images showing compressed CO2 being pulled into the leak. ..... 120 Figure 70: Cropped images illustrating CO2 flowing into the leak. ............................... 121 Figure 71: Cropped images showing poured CO2 being pulled into the leak. ............. 121 Figure 72: Cropped image of isobutane entering the leak. .......................................... 122 Figure 73: Cropped image showing residual heat from a candle enter the leakage. ... 122 Figure 74: Confirmation of leakage location using reference points and needle. Needle in place as background image, with no needle image overlayed at 50% opacity with reference points aligned(left) and no needle as background image with needle in place overlayed at 50% opacity with reference points aligned(right) Magenta is the background color and helps indicate which image is at reduced opacity. ................... 123 xv List of Tables Table 1: Similar variables in the two different control systems ...................................... 17 Table 2: Root-Mean-Square error of linear regression model ....................................... 20 Table 3: Root-Mean-Square error of regression tree model .......................................... 20 Table 4: Root-Mean-Square error of SVM model .......................................................... 22 Table 5: Root-Mean-Square Error of GPR models ........................................................ 24 Table 6: Root-Mean-Square error for NN ...................................................................... 25 Table 7: Top performing models in predicting distance output ...................................... 26 Table 8: Top performing models in predicting angle output........................................... 27 xvi List of Abbreviations carbon dioxide (CO2) ......................................................................................................................... 47 centimeters (cm) ............................................................................................................................. 9 composite manufacturing (CM) ............................................................................................................................. 3 composite manufacturing vacuum bag (CMVB) ........................................................................................................................ 1 frames per second (fps) ........................................................................................................................... 53 Gaussian process regression (GPR) ........................................................................................................................ 19 light emitting diode (LED) ......................................................................................................................... 35 meters (m) ............................................................................................................................. 27 millimeters (mm) .......................................................................................................................... 34 neural networks (NN) ........................................................................................................................... 19 root mean square (RMS) ........................................................................................................................ 20 support vector machine (SVM) ........................................................................................................................ 19 xvii Acknowledgements This research has been conducted in partnership with Convergent Manufacturing Industries. Yussuf Esmaeili has been instrumental in the research process through his limitless guidance, support and encouragement. I would like to express gratitude to Dr. Homayoun Najjaran for providing inspiration to pursue further studies as well as his willingness to take me on as a student as I pursued them. xviii Dedication Mom, I journeyed with you, and you journeyed with me. Thank you for showing me the way. 1 1 Introduction Composite materials are becoming widely used in all aspects of daily life. Globally there is a large shift from traditional materials to composites consisting of reinforcement fiber and resin matrices. The shift in manufacturing favoring composites has significant environmental impact. Take for example wind turbine blades, which have a service life of 25 years[1]. The manufacturing process involves many layers of reinforcement fiber saturated in a resin matrix. In order to ensure uniformity and good consolidation of layers, they are placed in a vacuum bag prior to curing. Once cured, the individual components become one inseparable material. The nature of the cured composites makes them challenging to recycle into their constituent parts. During the composite manufacturing vacuum bag (CMVB) process, a critical step foundationally important to the quality of the part is leak detection. In the CMVB process the individual layers making up a component are arranged in the desired order, before being sealed in a vacuum bag. A vacuum is applied to the bag and the bag is checked for leaks. If the bag is leak free, resin is introduced to the assembly or tool, this step is omitted if prepreg is used, and then the entire assembly is placed inside an autoclave to cure. The quality of products manufactured using CMVB processes is heavily dependent on a well-sealed, leak free vacuum bags. If leakages are not repaired prior to resin infusion, the resulting part will be defective. Possible defects include air bubbles, discontinuity in the resin, gaps, or resin traps leading to low quality, sometimes worthless, parts. This ultimately leads to increased material and labor costs which result from remanufacturing the defective part. Thus, it is critically important that leaks be located and repaired prior to curing as it mitigates defects resulting from leakages. 2 The challenge of detecting and locating leaks becomes complicated when large scale parts are manufactured. There is a direct correlation between the scale of the part and cost. The material cost of reinforcement fiber, resin, associated vacuum bagging supplies, and the labour costs for layup processes represent large scale investments when examining the process at an industrial scale. Although there are different techniques that can be used to detect a leak, the challenge of locating it on the vacuum bag is difficult. Most currently used techniques require the use of specialized equipment, materials that add complexity to the layup process, or skilled workers to locate leaks. The research endeavors to address the need for a simple, easily implemented method of definitive leakage localization. This research was conducted in partnership with Convergent Manufacturing Industries, a company that manufactures a flowrate sensor-based leak detection and localization system known as the COHO Gas Flow and Vacuum Leak Detection System. Their current system uses a proprietary algorithm to locate leakages. Although the system provides a rough prediction of leak location, there is still considerable error between the prediction and the actual location. The task of improving upon the flowrate sensor-based approach led to the development of a machine learning based framework. The shortcomings of this framework, including the inability to pinpoint leakages and reduced accuracy when multiple leakages are present, led to the need for a method of accurately pinpointing leakages which is the focus of this research. 3 1.1 Motivation The motivation behind this research stemmed from previous research conducted to improve the leakage location predictions of the COHO system using machine learning. While machine learning demonstrated an improvement in the accuracy of predictions, another method capable of pinpointing the precise location of the leak within the predicted area was required. The requirements of the composite manufacturing (CM) industry for well-sealed, leak free vacuum bags motivated the development of an accurate and economical leak localization method premised on the use of Schlieren imaging and machine learning. 1.2 Objective The aim of this research is to provide an alternative solution to conventional leakage detection methods by introducing a new technique of leak localization. The research evolved to determine whether Schlieren Imaging could be used to identify leakages in CMVB. 1.3 Outline The thesis is structured as follows: Chapter 2 discusses the literature used to formulate this thesis. More specifically, current methods addressing the problem of leak detection and localization in select CM techniques are examined. First, the CMVB process and significance of leakages is introduced. The methods used for leakage detection and localization including, pressure rise, ultra sniffer test, ultrasonic microphones, flowrate for detection, infrared thermography, piezoelectric 4 sensors, films and dye, flowrate for localization, and machine learning are discussed. My contributions as a research assistant are presented with an overview of the machine learning framework for leakage localization. Emphasis is placed on the machine learning framework as it represents a new method in the research space and my work on the project afforded me a more intimate knowledge compared to the aforementioned methods. Finally, the principles and theory of Schlieren imaging, as they pertain to my research, are discussed. Schlieren theory and the ability to visualize fluid flow is discussed. Chapter 3 outlines the methodology of the experiments. This section provides a detailed explanation of all aspects of the research procedure and experimental set up. A detailed explanation of equipment is also provided. Chapter 4 presents the results and findings of the research. This section also outlines the key limitations of the work. Chapter 5 offers evidence supporting the significance of the thesis. Additionally, future improvements and recommendations are given. 5 2 Review of Literature The purpose of this chapter is fourfold. First, it provides an overview of vacuum bag processes and the significance of leakages. The second purpose is to review detection and localization methods and technologies in the research space. The third purpose is to present my previous contributions to the research space. Finally, the theory and industrial application of Schlieren imaging techniques are discussed. 2.1 Vacuum Bag Processes and the Significance of Leakages Although CM is a broad topic encompassing many different methods, for example forged composites, and glass reinforced injection molding, the focus of this work specifically relates to vacuum bag-based manufacturing process, hence they will be discussed. A popular CM process, premised on the use of vacuum bags, is used for creating large-scale, high-quality parts and components[2]. CMVB processes are regularly used in both the aerospace and naval industries[3]. A typical CMVB layup consists of several layers including a release film, peel plies, reinforcement fabrics, breather fabric, and pre-preg that get laid over a mold. Once arranged in the desired order, a vacuum bag is attached to a base plate around the edges of the layup with sealant tape effectively isolating the component from the ambient environment. An example of a CMVB setup detailing the layers can be seen in Figure 1 below. 6 Figure 1: Basic vacuum bag assembly [4]. Once the assembly is complete with the bag sealed, a vacuum pump is attached to vacuum ports at various locations around the bag in order to remove all air from the bag. After verifying that the bag is free from leaks, resin is injected into the assembly. As the resin spreads through the assembly, it builds the matrix around the reinforcement fiber. Next, the assembly is left to cure in an autoclave, allowing the resin to harden and adhere to the fibers. The materials listed in Figure 1 all play an important role in the process. For example, the release film aids in separating the final product from the breather and the mold. The breather, a fibrous mat or fabric, controls air flow and acts as a reservoir to hold excess resin. The peel ply creates a uniform texture across the part and functions as a membrane, allowing resin and air to go through the reinforcement fibers. One of the most important steps in the CM process is vacuum bagging because it provides the foundation for creating high quality defect free components. Another way the vacuum bag assists in the process is by providing a clamping force to consolidate and compress the composite materials. The force is caused by the pressure difference between the contents of the bag and the ambient environment[5]. If a leakage is present in the system, it can cause defects in the finished part including air bubbles, discontinuity in the resin, gaps, resin traps, dry spots or poor 7 consolidation of layers[6]. These defects compromise the structural integrity of the part effectively rendering the part useless. The necessity for leak free bags means that leak detection is a crucial step in the vacuum assisted resin transfer molding process [7]. When the scale and cost of parts such as wind turbine blades are considered, the importance of both leak detection and localization becomes clear. 2.2 Methods of Detection The first group of methods is the more basic of the two groups. The methods discussed in this section only determine if a leakage is present in the bag but provide no definitive location for repair. The second group, discussed in the following section, is the more advanced of the methods. The methods in this group provide location predictions to varying degrees of accuracy and are a large improvement of the detection only methods. Despite the benefits of localization methods, not every vacuum bag has leaks, thus simple detection methods have merit as a means of inexpensively validating a bags integrity. 2.2.1 Pressure Rise The pressure rise method, one of the simplest methods of leak detection, requires a pressure gauge and a time keeping device[8]. The detection procedure as outlined by Haschenburger begins with the sealed vacuum bag assembly. First, a vacuum pump is attached, and the air is evacuated from the bag. Next, a pressure gauge is attached to the bag. The vacuum pump is then removed from the bag and an initial pressure reading is taken. The pressure is then monitored over a predetermined time period. A final pressure reading is recorded and used to determine the change in pressure[9]. A 8 pressure change exceeding the allowable threshold (5 to 15 mbar/min), is indicative of a leakage in the system. This method requires repetition of the test after each repair attempt is made deeming this process time consuming. 2.2.2 Ultra Sniffer Test The Ultra Sniffer Test[10], identifies the presence of leaks by monitoring helium concentration in the bag. This method was developed for use in plasma physics detecting leaks in stellarators. Due to the intended application, this method is highly sensitive and can detect leaks in the order of 10−11 mbar/s. Although sensitivity is an important evaluation metric, the sensitivity offered by the Ultra Sniffer Test is superfluous in vacuum bagging applications. Like the pressure rise method, the Ultra Sniffer Test method is incapable of providing leakage location. 2.2.3 Ultrasonic Microphone Ultrasonic microphones are used to detect leakages of varying sizes. This method is employed to detect leakages between the sealant tape and the vacuum bag; however, it can be used on the surface. Ultrasonic microphones detect leakages based on the sound they produce. The limitations of this method include, but are not limited to, the need for a skilled operator, limited practical frequency range, and the possibility of false positives results. The ultrasonic range coincides with the frequency generated by leakages. However, depending on the characteristics of the leak and the flow, lower frequencies outside of the range of the microphone may be generated. This phenomenon occurs predominantly when the flow at the leakage is laminar and not turbulent. Leakages are not the only source of ultrasonic sound in the vacuum bag 9 assembly as certain structural discontinuities and obstacles to flow may also produce sound in the ultrasonic range, ultimately misleading the operator. Some instances include vacuum ports and multiple pieces of breather. When used for surface detection, this process becomes time consuming as the entire area of the bag must be scanned at a distance of 30 centimeters (cm) from microphone to bag. This technique is most effective at the boundaries of the vacuum bag[11]. 2.2.4 Flowrate for Detection The application of flowrate sensors, a relatively new technology, addresses the problem of leak detection. The sensors record the flowrate in the vacuum lines. Monitoring the flowrate values offers insight into the leak-tightness of the vacuum bag. During the initial evacuation of the bag, the flowrate values will be high, but as the volume decreases the flowrate will settle to a value. When a leak is present, it allows ambient air to flow into the assembly and ultimately into the vacuum lines, meaning that the flowrate values will never settle to zero. The air flowing in will eventually reach equilibrium with the flow out through the vacuum lines and the flowrate will remain at an elevated rate. This can indicate a leakage in real-time. The real time capabilities alone improve upon the limitations of the pressure rise method. An advantage of the flowrate method is that each attempt at repair can be evaluated and a definitive conclusion about leak tightness can be made ultimately saving 15 minutes per attempt compared to pressure rise method[12]. 10 2.3 Methods of Localization Once a leak is detected, localization and repair follow. Although techniques for leak localization are many, there are three that are commonly used which are discussed below. They include infrared thermography, leak detection films and dye, and piezoelectric sensors. The first two methods, infrared thermography and leak detection films and dye utilize visual identification for leakage localization. The third method, the piezoelectric method, is a sensor-based approach. 2.3.1 Infrared Thermography Infrared thermography is an emerging technique which makes use of an infrared camera to visually locate leakages. This method excels at detecting leaks on the surface of the vacuum bag but has limited use for other types of leakages. The thermal camera is used to identify differences in temperature across the vacuum bag. When a leak is present, the area directly around it is cooled due to the JouleThompson-effect. The cold spot produced by the air entering the leak appears as a darker shaded region in a thermographic image. The cooling effect is most pronounced on the breather material, hence leaks in these regions are easier to visualize[13], [14]. Air entering a leakage travels to a region of lower pressure, where it expands and cools, ultimately appearing as a cold spot [9]. Leaks occurring at stiffening elements and folds in the vacuum bag film are particularly difficult to locate because they do not create as pronounced a thermal signature compared to leakages that occur when the vacuum bag is directly above breather material. 11 Stiffening elements and folds in the vacuum bag are problematic because leakages that occur at these features do not create as much cooling as leakages above the breather material thus the cold spot produced in the thermographic image is more difficult to identify. In practice, infrared thermography is a tedious process. One cannot simply stand from afar and observe an entire assembly through an infrared imaging device and notice leaks. Instead, the entire assembly must be imaged closely in order to be able to identify the faint dark regions surrounding leaks. 2.3.2 Piezoelectric for Localization Piezoelectric sensors have high resonance frequency, high sensitivity, and good stability. These properties make them useful tools for detecting and locating leakages in vacuum bags. When placed at specific locations on the surface of a vacuum bag the sensors identify small local pressure differences caused by leakages. The sensor data can be processed before using a localization method such as triangulation to predict the location of a leakage[15]. Piezoelectric sensors are capable of detecting and localizing leakages on the surface of the vacuum bag as well as successfully locating leakages on the sealing boundary. This method of localization does have limitations, the most significant being its inability to provide accurate location predictions when there are multiple leakages in the system. Another limitation of this method is the accuracy with which it predicts leakages. The location predictions made become more unreliable the closer the leak is to the edge of the tool. Incorporating piezoelectric sensors also adds complexity to the system by incorporating auxiliary equipment that is not integral to the vacuum bagging process. 12 2.3.3 Leak Detection Film and Dye Leak localization using oxygen sensitive films and dyes allow for leakages to be visually identified. When using films, a leak present in the assembly causes a visual change in the film. Air entering a leak reacts with the film to produce a visually identifiable change in the film’s appearance. Air entering causes visual changes in the film, allowing for the leak to be located and repaired. The use of fluorescent dyes is a more involved process compared to films which only require the addition of a single layer during the layup step. Locating leakages using dye involves the entire surface of the bag being coated while vacuum is applied to the assembly. The dye is then removed from the surface of the bag. When the bag is coated in dye leakages will pull the dye into the bag, staining the breather material. Leakages appear as dyed regions inside the vacuum bag, with the effect most apparent in the white breather. Despite allowing for visual identification of leak location, leak localization using film and dye have two main disadvantages. First, cost and complexity are added to the overall process by either requiring an extra layer in the layup process or an entire extra step of coating the bag in dye. Second, both methods are time consuming as they require a visual inspection of the entire assembly[9]. 2.3.4 Flowrate for Localization The detection and localization methods previously outlined including pressure rise, ultrasonic microphone, ultra-sniffer test, flowrates, infrared thermography, piezoelectric sensors, leak detection dyes and films, are similar in that they require extra equipment not integral to the vacuum process with respect to detection and leakage localization. Whether an extra layer in the layup, additional sensors, or infrared cameras, the 13 methods discussed above all require additional steps that must be added to the basic CMVB process even in the absence of leakages. The use of flowrate sensors to detect and locate leakages utilizes an extra piece of equipment in the way of flowrate sensors, however these sensors differ in that they monitor the vacuum process that is already occurring and are external to the assembly. The flowrate sensors monitor a primary or direct property of the process, as opposed to a secondary property like cooling caused by a leak, or stains resulting from a leak. Additionally, incorporating flowrate sensors does not add complexity at the tool. Instead, the sensors are placed in line with the vacuum pump which is independent of the tool. The use of flowrate sensors for the purpose of leakage detection is accurate and rapid, however the data from these sensors has much more potential. The proximity of a leakage to a vacuum port directly impacts the flowrate value seen at the vacuum port. The closer the leak is to the port, the higher the flowrate. Using the relationship between flowrate values and port to leakage distance, flowrate values from different ports can be compared to allow for leakage locations to be predicted. One company that makes use of flowrate sensors for leak localization is Convergent Manufacturing Industries. They manufacture a complete vacuum bagging system called the COHO 601. The COHO 601 uses six inline flowrate sensors, one for each of its vacuum lines. The sensors have a sampling frequency of 1 Hz. While the COHO system does include a proprietary localization algorithm, it does not always provide the most accurate location predictions. The methods outlined above are illustrative of what is broadly used in vacuum bag CM. Although many techniques exist, few if any, can reliably locate leakages independently. In fact, Haschenburger et al. compared various detection and localization methods, 14 many of which have been discussed here. The evaluation was based on metrics including cost, time, handling component size, and detection size[9]. The results of the comparison indicate that many of the methods tested were either time consuming, expensive, required the presence of a skilled operator to be effective, added complexity, or some combination of these qualities. Thus, an efficient, cost effective, and automated method is needed. An unlikely technology that demonstrates potential to excel for this application is machine learning and artificial intelligence. 2.3.5 Machine Learning Machine learning is a field of inquiry rooted in the development of methods capable of leveraging large amounts of data to improve the performance of some set of tasks. Machine learning can be broken down into two main categories, supervised and unsupervised learning. Unsupervised learning involves problems such as clustering and dimensionality reduction. Supervised learning focusses on classification and regression. Both types of learning have their place. Regardless of the type of machine learning one wishes to implement, they all share one commonality, the need for large datasets for the agent to train or learn with. In the context of leakage detection and localization, supervised learning techniques are most useful. In supervised learning labelled training data consisting of inputs and outputs is used to learn a function that relates input to output based on training examples. The data generated through monitoring flowrate values during the vacuum process can be used to train a regression-based machine learning agent to make predictions about leakage locations. A regression-based machine learning model is well suited to the application of leakage location because it can take flowrate values as 15 input, intuit meaningful relationships from the data, and make predictions as to leakage locations. This is realized by iterative optimization of a cost function, this in turn allows the machine learning agent to make predictions for new unseen inputs. Esmaeili et al. proposed the application of machine learning for the purpose of locating leakages in vacuum bags[4]. The localization framework is outlined in this section. The application of regression-based machine learning techniques for leakage location, and in fact all machine learning techniques, is hindered by one common obstacle, the need for data. For many applications there is an abundance of readily available datasets relating to diverse fields, or the process can be easily simulated allowing for data to be synthesized. Unfortunately, the field of leakage detection and localization as it relates to CM does not have this luxury. Since leakage localization is an industrial process and tends to be a niche application of machine learning, one of the initial hurdles in the implementation of a machine learning based leak localization framework is the lack of data. Two strategies addressing this include physical experimentation, and modelling and simulation. 2.3.5.1 Simulation Data is foundational to machine learning and for a machine learning based leak localization system to be realized, large amounts of data describing the process must be collected. For the purpose of leak localization this would equate to manually introducing leakages to vacuum bags and recording the flowrates. This process would need to be repeated hundreds of times across many different vacuum bags to develop a large, thorough dataset that describes flowrate values for many leakages. Physically generating datasets at the scale required for machine learning is not practically feasible. 16 Hence, a fast, reliable, and inexpensive simulation method that could be used to synthesize large amounts of data was sought to meet the data requirements. Despite considerable work being done in the area of modelling resin flow mechanism and pattern in the mold, it is not of particular interest for this research because it does not provide models or flowrate predictions when leakages are present in the system[16]. Although numeric simulation software, such as COMSOL, Flow3D, and Ansys, is widely available, they share a common limitation. Numerical simulations are notoriously computationally intensive. On a modern high powered consumer grade computer, the computation time can exceed one day for a single configuration. The time required to generate the volume of data needed is impractical with conventional numerical simulations. Additionally, the numeric methods result in complex modeling of nonlinearity, as well as superfluous details for this specific application. Knowing the practical limitations of numeric simulation software, Esmaeili et al. developed a new method of simulation based on an analogy between the vacuum bag system and electrical circuits that would allow for rapid, computationally practical simulations [4]. The similarities between electrical and mechanical components and their effects on a system, as seen in control theory, was foundational in the development of the model. A simple example that illustrates the commonalities between electrical and mechanical circuits can be seen in second order mass spring dampers, and resistor inductor capacitor circuits. Despite being fundamentally different, they both behave the same way. Similarly, the flowrates of vacuum ports can be simulated using the current in an electrical circuit. To maintain simplicity in the model, a zero-order model consisting only of resistors was selected. 17 The relationships between the two systems can be seen in Table 1. In an electrical circuit a voltage between two nodes causes a current between them. In the physical system, a pressure difference between two points causes flow from high pressure regions to low pressure regions. The flow is determined by the viscous resistance of the breather whereas in the electrical circuit the current is determined by the resistance of the resistors. Table 1: Similar variables in the two different control systems Real System Unit Simulation Unit Flowrate (COHO sensors) L/min Current (Ampere meter) A Pressure difference Pa Voltage difference V Breather viscous resistance N.s/m2 Resistance Ω Implementation of this simulation technique begins with the construction of a model. As previously stated, the model consists of three main components, the resistor network, ammeters and grounds. The resistor network is built in such a way that it reflects the geometry of the tool to be modelled. The resistors used to build the network are connected in parallel which causes the total resistance to decrease. This is analogous to parallel flow channels in a vacuum bag system. The electrical circuit model can be seen in Figure 2 and Figure 3. The circuit was designed so that the addition of resistors would increase the precision and effective resolution of the simulation, which is equivalent to using finer meshes in other numerical simulations. 18 Figure 2: Resistor network which models breather. Figure 3: Ammeters and grounds which represent vacuum ports. Every two points, in both systems, have some resistance between them and this resistance increases as the distance between the points increases. The resistors were initially set to 100 Ω. This value was chosen arbitrarily because the network resistance is normalized and becomes dimensionless during data preprocessing. The simulation method proposed by Esmaeili et al. has been experimentally validated, proving its efficacy for the synthesis of flowrate data. The simulation method has been used to produce a dataset containing 554 samples collected from various geometries and conditions. This dataset can be used to build a regression-based machine learning algorithm. 19 2.3.5.2 Training Following the creation of a dataset, a machine learning algorithm can be created. The first step in the process is to segregate the dataset into two smaller datasets, one for training the agent and one for validating the predictions. The training set will be the larger of the two, and will contain 80% of the original dataset, the remaining 20% is allocated for the validation set. The dataset for training and validation was collected on a 90 by 90 cm breather. An additional dataset to evaluate generalizability was generated on an 82 by 73 cm breather. Each sample in the dataset contains the following information, six flowrate values which serve as features, and coordinates of the leak which are the labels. The polar coordinate system is used for the location predictions, each location on the breather can be described by a distance r and an angle φ from the origin. For ease of implementation the origin was located at port six. Different machine learning models can be trained to accurately model the vacuum bagging system. Five machine learning models were tested in the development of the framework including, linear regression, regression tree, support vector machine (SVM), Gaussian process regression (GPR), and neural networks (NN). Linear regression is one of the simplest functions capable of modeling the input output relationship of the system. Esmaeili et al. tested four variations of linear regression models, linear, linear with interaction terms, robust linear, and stepwise linear. The linear model is trained with the standard linear regression function which takes the form. 20 𝑦 = 𝑏0 + 𝑏1𝑥1 + 𝑏2𝑥2 + . . . + 𝑏6𝑥6 Equation 1 The linear regression with interaction model builds on Equation 1 by adding an interaction term, which increases the complexity. The equation takes the following form: 𝑦 = 𝑏0 + 𝑏1𝑥1 + 𝑏2𝑥2 + . . . + 𝑏6𝑥6 + 𝑏12𝑥1𝑥2 + 𝑏13𝑥1𝑥3 + . . . + 𝑏56𝑥5𝑥6 Equation 2 The robust linear model improves upon the standard function by increasing the function’s ability to handle outliers. Finally, the stepwise model takes a different approach to training, Different terms are added and trained in steps. The prediction performance of the regression models is presented in the following table. Table 2: Root-Mean-Square error of linear regression model r (cm) RMS error φ (°) RMS error Linear model Validation Test Validation Test Linear 5.4 3.25 14.85 13.22 Interaction linear 4.2 2.5 11.15 14.38 Robust linear 5.4 2.39 19.19 20.21 Stepwise linear 4.3 2.39 11.36 13.50 One type of decision tree, called a regression tree, is useful when the input output relationship is too complicated to model using a linear function[17]. Esmaeili et al. made use of three regression tree models with varying leaf sizes, fine, medium, and coarse, corresponding to 4, 12, and 36 leaves respectively. The root mean square (RMS) error for the regression tree predictions can be seen in the following table. Table 3: Root-Mean-Square error of regression tree model r (cm) RMS error φ (°) RMS error 21 Tree type Validation Test Validation Test Fine tree 4.4 4.1 11.52 6.64 Medium tree 4.8 4.7 12.27 5.91 Coarse tree 7.1 5.1 13.76 9.67 The third model, SVM regression, is a robust supervised learning method. SVMs use hyper-planes as decision boundaries [18]. The linear SVM function is defined as: 𝑦 = 𝑏0 + 𝑏1𝑥1 + 𝑏2𝑥2 + . . . + 𝑏6𝑥6 = 𝑥′𝑏 + 𝑏0 Equation 3 The margin of tolerance, which is equivalent to the distance to the boundary is: ∀𝑛 ∶ |𝑦𝑛 − (𝑥′𝑛𝑏 + 𝑏0)| < 𝜖 Equation 4 The loss function is defined as follows, 𝐿𝜖 = {0|𝑦 − (𝑥′𝑛𝑏 + 𝑏0)| − 𝜖 𝑖𝑓 |𝑦 − (𝑥′𝑛𝑏 + 𝑏0)| < 𝜖𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒 Equation 5 The ability to initialize SVM with different kernels is what contributes to its versatility. Esmaeili et al. created six SVM models with different kernels. The six kernels were linear, quadratic, cubic, fine Gaussian, medium Gaussian, and coarse Gaussian. The results of the training can be seen in the following RMS error table, which shows the r and φ prediction error for the six SVM models. 22 Table 4: Root-Mean-Square error of SVM model r (cm) RMS error φ (°) RMS error SVM model Validation Test Validation Test Linear 5.4 6.8 16.18 15.26 Quadratic 4.2 5.1 11.59 9.81 Cubic 3.1 4.3 9.09 17.61 Fine Gaussian 4.7 5.9 9.92 8.58 Medium Gaussian 3.3 3.8 10.11 10.64 Coarse Gaussian 4.5 5.3 13.81 11.90 Examining Table 4, the fine Gaussian kernel resulted in the lowest prediction error over validation and test sets. The medium Gaussian kernel was the best at predicting the distance r. The Linear kernel showed high bias and is unsuitable for modelling this problem. The cubic kernel is too complicated for the problem and results in overfitting. The quadratic kernel performed well, with a proper trade-off between accurately modelling the problem and not overfitting to the data. The fourth model used, is GPR based. Like SVM models, GPR models also use kernels. What sets GPR models apart from SVM models is that they are non-parametric and probabilistic[19]. A Gaussian process is a collection of random variables each possessing a Gaussian distribution. The mean and covariance function can fully describe a Gaussian process. The functions are defined as: 𝑓 ∼ 𝐺𝑃(𝑚, 𝑘) Equation 6 The previous equation means that 𝑓 is distributed as a Gaussian process with mean function 𝑚 and covariance function 𝑘 which is equivalent to a kernel. Esmaeili et al. selected four kernels, rational quadratic, squared exponential, matern 5/2 and 23 exponential. The squared exponential kernel is one of the most widely used kernels for Gaussian process modelling. Its function takes the form[20]: 𝑘(𝑥𝑎, 𝑥𝑏) = 𝜎2𝑒𝑥𝑝(−||𝑥𝑎 − 𝑥𝑏||2/2𝑙2 ) Equation 7 In the above formula, σ2 is the overall variance and 𝑙 is the lengthscale parameter which specifies the kernel width. The squared exponential kernel returns a smooth prior on functions sampled from the Gaussian process. A kernel that produces similar results to the squared exponential kernel is the rational quadratic kernel. It can be interpreted as an infinite sum of different exponentiated quadratic kernels with different lengthscales. The function takes the form seen below. 𝑘(𝑥𝑎, 𝑥𝑏) = 𝜎2 (1 +||𝑥𝑎 − 𝑥𝑏||22𝛼𝑙2)−𝛼 Equation 8 In the function above α is the scale-mixture. The third kernel, the matern (v = 5/2) kernel has the benefit of using both long and short lengthscales. This enables extrapolation, which means that it can learn from unseen regions of the function’s domain[21]. The training results of all GPR kernels can be seen in the following table. 24 Table 5: Root-Mean-Square Error of GPR models r (cm) RMS error φ (°) RMS error GPR model Validation Test Validation Test Rational quadratic 2.3 4.4 9.02 10.07 Squared exponential 2.3 4.6 9.03 12.12 Matern 5/2 2.3 4.3 8.45 11.42 Exponential 2.6 3.4 8.6 10.36 Based on the results in the table above, it is clear that the models are all overfitting. This is corroborated by the fact that the test error for the distance value r is approximately double the validation error. It is worth noting that the GPR model’s performance is satisfactory for predicting angles. This is likely because the angle prediction requires a more complicated model than the distance prediction. The final group of models Esmaeili et al. employed were NN. NN were first developed by McCulloch and Pitts in 1943 for the purpose of artificial intelligence[22]. Recently, NN are facing a resurgence in popularity for machine learning and have been successfully applied to many problems including classification, clustering, pattern recognition, and prediction. Since the input-output relationship of the function is not overly complicated, deep and complicated NN are not necessary and could result in overfitting. Esmaeili et al. elected to train five shallow NN. The general architecture of NN can be seen in Figure 4 below. 25 Figure 4: General network architecture trained for the problem. The models take the six flowrates as inputs at the first layer and have a regression output layer. The five models are narrow, medium, wide, bi-layered, and tri-layered. The narrow model consists of 10 neurons in the hidden middle layer. The medium model is made up of 25 neurons in the hidden middle layer. The wide model has 100 neurons in the hidden middle layer. The bi-layered model is made up of two hidden layers with ten neurons each. Finally, the tri-layered network is composed of three hidden layers with 10 neurons each. The results of the NNs predictions can be seen in the RMS error table below. Table 6: Root-Mean-Square error for NN From the table above, most models, except for the narrow model, are susceptible to overfitting with respect to the distance prediction. This is supported by the low validation, yet high test, error seen in these models. With respect to angle predictions r (cm) RMS error φ (°) RMS error NN model Validation Test Validation Test Narrow 3.4 4.1 11.68 13.89 Medium 2.7 4.7 9.15 12.20 Wide 2.5 4.8 9.03 8.68 Bi-layered 2.8 5.5 10.06 10.48 Tri-layered 2.7 4.2 9.00 9.03 26 the more complex models performed better. The tri-layered and wide model performed the best at predicting the more complicated angle output. Esmaeili et al. tested 22 different machine learning models in the development of a machine learning based leak localization framework. Following the initial experiments, the model’s generalizability was evaluated. The previous training data, although unseen by the trained models, was from a square 90 by 90 cm breather. To evaluate the generalizability of the models, a new data set was created based on an 82 by 73 cm breather. The predictions from the models needed to be scaled to the size of the new breather in order to make valid predictions. The top performing models, with respect to generalizability, for the distance prediction are fine Gaussian SVM, medium Gaussian SVM, exponential GPR, and the narrow NN. The RMS error for each of these models can be seen in the following table. Table 7: Top performing models in predicting distance output Method RMS error (cm) Fine Gaussian SVM 8.2 Medium Gaussian SVM 8.3 Exponential GPR 8.3 Narrow NN 8.7 The top performing angle prediction models are fine tree, rational quadratic GPR exponential GPR and bi-layered neural networks. The results of those models can be seen in Table 8. 27 Table 8: Top performing models in predicting angle output Method RMS error (°) Fine Tree 5.7 Rational quadratic GPR 6.4 Exponential GPR 6.9 Bilayered neural network 7.5 It is worth noting that the simple models excel at predicting the distance and the complex models are more adept at predicting the angle. This is due to the angle output being more complicated than the distance output and hence better predicted by a similarly complicated model. Although the machine learning based framework outlined by Esmaeili et al. can predict the location of leakages with a satisfactory level of accuracy, the framework still has an RMS error of around eight cm and around six degrees on an 82 by 73 cm breather. On a breather of this size, it is easy to manually locate the leakage in the reduced search area provided by the framework, however when the scale of industrial vacuum bags is considered, the nearly 10% error represents a much larger area. For instance, a vacuum bag assembly for a wind turbine can exceed 100 meters (m) long and the 10% error equates to a considerably sized search area. Since manually searching an area requires using your hand to cover different spots in the search area until the flowrates begin to decrease, it is too time consuming to conduct the final search manually for search areas larger than 1 m2. Large search areas require an additional method of localization and there is potential to incorporate other sensors or localization techniques to further improve the framework Esmaeili et al. have developed. One 28 technique of particular interest is Schlieren imaging, which leverages optics and physics principals to visualize density changes in transparent media. 2.4 Schlieren Exploring the methods of localization and detection presented above identified a common theme shared by all, with the exception of the flowrate method. The methods discussed rely on a secondary characteristic of a leakage, for example, detecting the sound produced or waiting for the pressure to rise, indicating a leak. When considering possible ways of detecting a leak using a primary characteristic like the flowrate method, I recalled an educational video that demonstrated a technique used to visualize air currents[23], [24]. The technique’s ability to visualize air currents in transparent media guided my research towards Schlieren imaging. Schlieren imaging is a technique that makes use of optical elements to visualize flow in transparent media such as air. Schlieren imaging has seen many applications since it was first brought to prominence by German physicist August Toepler in 1864, however, evidence of the use of Schlieren techniques dates to the 17th century[25]. Notable contributions to the field were made by Robert Hooke in his book, Micrographia, Christiaan Huygens, and Leon Foucault, who collectively used Schlieren principles as tests for optical devices. After Toepler’s development of a Schlieren imaging apparatus, it opened the door for further research into applications and improvements in technique. One notable contributor to applications and new techniques was Hubert Schardin, whose contributions are still being explored. 29 The optical technique derives its name from the German word schliere, or striae, which is an allusion to the streak-like appearance of fluid flow visualized with the system[26]. First used for studying supersonic motion, Schlieren imaging is currently being applied in the aerospace, glass manufacturing and material testing industries to name a few. One area that appears to be an obvious application of Schlieren imaging is leakage localization[27][28]. A Schlieren system would allow for the actual fluid flowing into a leak to be visually identified ultimately allowing for leaks to be definitively located and repaired. Upon initial review of the literature on Schlieren imaging, it was found that there is very little pertaining to my research topic, using Schlieren imaging for the localization of leakages in CMVBs. Since the use of Schlieren imaging for leak detection seems logical, the lack of literature supporting its use for vacuum bag leak localization was surprising. I began exploring Schlieren broadly, developing an understanding of what the individual components in a system included, as well as the role they played in creating the optical phenomena. As one can imagine, when utilizing principles of physics, there are many ways a system can be realized. The more complex systems rely on multiple lenses and mirrors, however a classical, or single mirror Schlieren system requires a small light source, a concave parabolic mirror, and a razor blade[25], [27], [29]–[31]. These three components can unlock the ability to visualize small variations of density within transparent fluids. Schlieren Imaging is based on classical optical principles, in particular Snell’s Law which states that when light encounters matter, its speed decreases[26]. In a vacuum, light travels at a constant velocity, with no directional changes, whereas in instances of 30 inhomogeneity such as fluids in motion refraction occurs. Differences in refractive indices, or more technically, the gradient of refractive index, are what cause the schliere and give Schlieren images their distinct appearance[26]. In order to apply the technique to solve a problem, one must first understand the underlying principles of Schlieren imaging. The technical report titled Principles and Techniques of Schlieren Imaging Systems, by Amrita Mazumdar presents a succinct introduction to the theory behind Schlieren imaging. Mazumdar provides a mathematical proof as a basis for quantitative analysis. More importantly, for the purpose of this research, the final two equations below explain that the effects seen in Schlieren images are due to changes in the gradient of the refractive index in the test area. Mazumder states: To present a simpler deconstruction of Schlieren light refraction, we assume a negative vertical refractive-index gradient 𝜕𝑛 𝜕𝑦⁄ < 0, and no gradient in the x- or z-directions. A planar light wave, initially vertical, becomes displaced after propagating through a schlieren object. If it covers a differential distance in differential time, Δ𝑧 Δ𝑡⁄ , it is refracted through the differential angle Δ𝜀. The refractive index is defined as, 𝑛 = 𝑐 𝑣⁄ , where n is the refractive index, c is the speed of light in a vacuum, and v is the local light speed. We find: Δ𝜀 =𝑐𝑛2−𝑐𝑛1Δ𝑦Δ𝑡 Equation 9 31 Combining this expression with an expression for differential time, Δ𝑡 = Δ𝑧𝑛𝑐, and simplifying terms, we find: Δ𝜀 =𝑛𝑛1𝑛2(𝑛1 − 𝑛2)Δ𝑦Δ𝑧 Equation 10 We can simplify this by letting all finite differences approach zero and simplifying 𝑛𝑛1𝑛2 to 1𝑛, and obtain 𝑑𝜀𝑑𝑧=1𝑛𝑑𝑛𝑑𝑦 Equation 11 If we use small angle approximation to postulate 𝑑𝜀 is equal to 𝑑𝑦 𝑑𝑧⁄ , we obtain 𝜕2𝑦𝜕𝑧2=1𝑛𝜕𝑛𝜕𝑦 Equation 12 relating the curvature of a refracted ray to the magnitude of the refractive-index gradient. Schlieren images can be found by integrating the ray curvature of light rays in optical inhomogeneities in the appropriate directions: 𝜀𝑦 =1𝑛∫𝜕𝑛𝜕𝑦𝜕𝑧 Equation 13 These equations can also be used to find the component of the 2-D gradient in the x-direction by replacing the y’s with x’s. Equation 12 indicates it is not the refractive index n causing ray deflection, but the gradient ∂n/∂y of this refractive index. Additionally, Equation 12 and Equation 13 show light ray deflections bend towards regions of higher refractive index.[26] 32 The mathematical proof outlined above provides a deeper understanding of the fundamentals of refraction governing Schlieren imaging. Chapter 2 lays the foundation for the development of a novel method of leak localization. Reviewing the literature relevant to leak localization and detection with respect to CMVBs offers an understanding of vacuum bag processes, the significance of leakages, and the importance of detection and localization methods. Analyzing the methods used in industry supports the development of a localization technique able to pinpoint the location of a leakage based on a primary characteristic, namely flow. The need for a method based on a primary characteristic, coupled with the ability of Schlieren imaging to visualize flow in transparent media, informed the direction of this research. 33 3 Methodology This research stems from the need for an alternate method of locating leakages on vacuum bags. While the conventional methods discussed in Chapter 2 can either detect or locate a leakage, they rely on secondary characteristics including pressure, sound, or color changes. The flowrate-based localization method relies on a primary characteristic, namely the flow caused by air entering the vacuum bag at a leak. An accurate method of localization to definitively locate a leak in the location output of the machine learning algorithm was sought. Considering the flowrate method uses primary characteristics, another method based on flow would integrate well. A leakage, in simple terms with respect to vacuum bags, is fluid flow from the region of high pressure outside of the vacuum bag to the region of low pressure inside the vacuum bag. Familiarity with Schlieren imaging led to the realization that it was in fact possible to visualize flow in transparent media. However, the question remained as to whether Schlieren imaging could be used to locate leakages in vacuum bags. 3.1 Experimental Setup In order to determine whether Schlieren imaging could be used to locate leakages in vacuum bags, experiments were conducted to develop a working Schlieren system. Preliminary experiments were conducted prior to the actual experiments to determine if Schlieren imaging could visualize leakages in vacuum bags. For simplicity, as well as cost benefits, a classical or single mirror Schlieren system was chosen as the basis for experimentation. A classical Schlieren system consists of a converging optical element, a light source, a light block, and a screen or imaging 34 device[25], [32]. The particularities of each of these elements as they pertain to my experimental system are outlined below. 3.1.1 Converging Optical Element When assembling a classical Schlieren system there are two options for the converging optical element. One option is convex lenses, such as the biconvex lens or the Fresnel lens. The other option is concave mirrors[26]. Regardless of the element chosen, it must meet basic criteria, including being physically large in surface area and having a focal length greater than 1000 millimeters (mm). Sourcing a lens meeting these criteria is difficult, however, it is much easier to find a mirror that satisfies the criteria. A common source for these mirrors is telescopes, more specifically, the primary mirror from a reflector type telescope. The mirror used in this Schlieren system was purchased second hand and therefore the manufacturer specifications are unavailable. The focal length of the mirror was experimentally determined to be approximately 1160 mm. Since telescope mirrors typically come in 1000 and 1200 mm focal lengths[33], [34], it is likely that the true focal length is 1200 mm with a mirror diameter of 200 mm and is equivalent to[35]. 3.1.2 Light Source The light source provides the light used to visualize the density gradients present in the test area of the system. The light source used must be very small and be highly collimated, so that only the refraction caused by the density gradient is visualized. The simplest means of acquiring a light source such as this is by using a 5 W light emitting 35 diode (LED) behind a pinhole aperture. The light source for this system is a green LED placed behind an aluminum foil sheet with a pinpoint hole. 3.1.3 Light Block The purpose of the light block is to increase the sensitivity of the system. The finer the light block edge, the more effective it is at cleanly cutting the focused beam in half. The light block used in this experiment is a razor blade. 3.1.4 Imaging Device The imaging device can be as simple as a screen for viewing the Schlieren image, however for the purpose of this research a means of recording the image was needed. This necessitated the use of a camera. The camera used for this experiment is a Nikon D7100 DSLR body paired with a NIKKOR 18-200 mm lens. The body features a 23.5*15.6 mm CMOS sensor capable of capturing 24.1 megapixel images which should provide suitable quality images[36], [37]. The components introduced above are the final components selected for use in this system, however, they are not the only components tested. Prior to evaluating the applicability of Schlieren imaging for leak detection, a working Schlieren imaging system was required. The iterative development of the system is discussed below. 3.2 Iterations of the System The first iteration of a Schlieren imaging system was composed of components borrowed from other UBC engineering labs. A converging lens was used, and despite having an adequate focal length, the diameter of the lens proved too small and 36 Schlieren images could not be produced. The first iteration can be seen in Figure 5 below. Figure 5: First iteration of Schlieren system using a converging lens and close up of the lens. © Brett Cosco CC BY-NC-ND 4.0. The second iteration made use of a concave mirror from an optical demonstration kit seen in Figure 6. It had unknown specifications, but it had a very large diameter of 0.4 m and the focal point was experimentally determined to be 0.6 m or 600 mm. Due to the lack of manufacturer specifications, it is not known whether this mirror was parabolic. This iteration used a bare LED as a light source, and a Nikon 1 J1 digital mirrorless body paired with a 10-30 mm NIKKOR lens as the imaging device. 37 Figure 6: Second iteration of the system using concave mirror. © Brett Cosco CC BY-NC-ND 4.0. The light block was omitted during the initial iterations, as it is not mandatory to view Schlieren like images. Additionally, the light block adds another layer of complexity when aligning the system. When the light block is omitted, the system produces shadowgraphs which are very similar to Schlieren images, however they are sensitive to variations in the second derivative of density[29]. This iteration resulted in improvements over the first in that light from the LED travelled through the system and was captured by the camera. The literature that outlines the assembly and alignment of Schlieren systems references being close to alignment when the mirror becomes illuminated and appears as a bright object in the camera[38]. It is challenging to describe such abstract phenomena with respect to how the mirror behaves in the setup. First, it is important to describe what one would see through the camera when not aligned. It is easy to think that one would observe the scene (what appears in front of 38 the camera) in a similar fashion to how our eyes might view the scene, but this is incorrect. Since the light source must pass through a small aperture, it is exceptionally dim when compared to the ambient light present from the sun or the lights in a room. The camera settings must be configured to “ignore” the ambient light and focus only on the light of interest. When the camera settings are configured as such, the image seen in the camera becomes almost black and it is difficult to determine exactly how the image is composed. One benefit is that when the system nears proper alignment, the mirror appears as a bright white spot roughly the size of the mirror, however this depends on how the lens is focused. Although the second iteration did not produce the desired images, it proved to be one step closer to a working Schlieren system. For the third iteration the novelty mirror was exchanged for a concave parabolic mirror from a reflector telescope. Mirrors of this sort are built to high tolerances and have extremely thin highly reflective coatings. When used in telescopes, they have a finite life span before they become unsuitable for the application. A local telescope store was able to provide an unserviceable telescope mirror for $50.00. The new mirror was purchased used and hence did not come with any form of specifications. The following information was provided by the previous owner at the time of purchase. The mirror originated from an old, albeit high quality telescope, with a focal length exceeding 1000 mm. This mirror represents a critical point in the research, as it is the component that first unlocked what felt like a portal into another dimension. Facing frustration from the lack of results, as well as the simplicity with which the literature describes setting up a Schlieren system, I elected to go back to basics and focus on making the optical phenomena occur. This meant changing the imaging device from a camera to a screen and foregoing the razor 39 blade light block all together. A screen is much easier to work with because it allows for a large margin of error with alignment, whereas with a camera, one or two mm of movement is enough to make a perfectly aligned system unable to view the optical phenomena. The third iteration utilized the new mirror, a green LED housed behind a pinpoint aperture, and a white wall for the screen. The mirror was placed on a tabletop parallel to the screen, and the LED was positioned approximately 1 m away from the mirror pointed in the direction of the mirror. Once the LED was illuminated, minor adjustments were made until a green circle was projected onto the wall. Most literature pertaining to constructing a Schlieren system cite candles as good test subjects when working towards aligning the system because their effect under Schlieren imaging conditions is so pronounced. A candle was placed in the test area in front of the mirror in order to evaluate whether the green circle projected on the screen would show the density variations caused by the candle. Initially, the image viewed on the screen was inverted. Changing the distance between the LED and the mirror had the effect of altering the size of the image. Decreasing the distance made the inverted image larger. Increasing the distance made the image smaller to a point, but then began making the image larger. According to optical properties of mirrors this was caused by moving the light source from inside to outside of the focal point. Once the image began enlarging, it became apparent that the image was no longer inverted. Once the LED was correctly positioned to generate a properly oriented image, the first Schlieren like images were produced and captured. The first schlieren like images of the thermal plume created by a candle can be seen in the following figures. 40 Figure 7: First Schlieren like images captured with the system. Figure 8: Thermal plume of candle projected onto screen. Figure 9: Frame by frame sequence showing thermal plume. The fourth iteration built on the third by replacing the screen with a smart phone camera. Due to the lack of mounting points on typical smartphones, sustained alignment through a fixed mount was difficult. Some videos were captured with this iteration, but they are too unstable to be useful. The smartphone was exchanged for the Nikon 1 J1 for the fifth iteration. In the following figures examples of poor system alignment and good system alignment are provided. 41 Figure 10: Examples of poor system alignment. Figure 11: Example of thermal plume of candle under good system alignment. Iteration five incorporated a tripod as a mounting platform for both the camera and the light source. This setup allowed for the first good quality Schlieren images to be recorded. The images recorded by this iteration can be seen in Figure 12 and Figure 13. It also demonstrated that all the components of the system worked together. Upon examining the images captured, it became clear that although everything worked as intended, a different lens was required. The 10-30 mm lens did not provide enough 42 zoom. Even in the 30 mm setting, the mirror did not fill the image captured by the camera, which is to say that much of the captured image was black. Since the camera sensor has a finite resolution, it is important for the area of interest (the mirror) to occupy as much of the image in the camera as possible because the amount of the image the mirror takes up is directly proportional to the amount of sensor being used to capture that image. By maximizing the use of the imaging sensor, more information was captured, resulting in higher resolution in the area of interest. Figure 12: Images captured with the 1 J1 by system that has been mildly misaligned. Figure 13: Images captured with the 1 J1 when the system is properly aligned. The sixth iteration of the system addressed the limitations of the previous system by changing the imaging device to a Nikon D7100 with an 18-200 mm lens. Comparing 43 specifications between the two imaging devices shows that the D7100 is a substantial improvement over the previous 1 J1. The J1 has a 13.2*8.8 mm 10.1 megapixel CMOS sensor. The D7100 has a much larger 23.5*15.6 mm sensor with 24.1 effective megapixels[39], [40]. The increase in megapixels, as well as the 18-200 mm lens meant that high quality images with finer detail could be captured. A comparison between the image quality of the 1 J1 and the D7100 is provided in Figure 14 and Figure 15 below. Although the images captured were adequate in quality, the lack of a fixed mounting point for the light source meant that alignment was easily disturbed. Figure 14: Sample of 1 J1 image quality when cropped to candle flame. Figure 15: Sample of D7100 image quality when cropped to candle flame. A mounting plate with a rail system was designed to allow for the camera and light source to be attached to a common base. See Figure 16 below. This marked the seventh iteration of the Schlieren imaging system. The mounting plate provides the 44 same frame of reference for both the light source and the camera, which makes alignment far easier than independently manipulating the light source and camera’s position[30]. The rail system allows for independent adjustment in the x y and z directions making initial alignment more efficient. The light source is also clamped to the rails to prevent misalignment caused by small disturbances. The images captured with this system were of the desired quality, although the system was still lacking one important component. Up to this iteration, the objective was the development of a working Schlieren system at the expense of quality of Schlieren effect produced. This meant foregoing a camera, using a screen, and removing the light block in early iterations. Once a suitable system was achieved in the seventh iteration, the final component, the light block, was introduced. The addition of the light block marked the eighth and final iteration of the Schlieren system used for this research. The light block was implemented using a soldering third hand that could be manipulated to hold the razor blade in a specific position. The ability to maintain the razor blade’s position precisely at the focal point of the light, perfectly blocking half of the light from entering the lens is important for improving the sensitivity of the system. The previous iterations that lacked the light block did not produce true Schlieren images, instead, they produced shadowgraphs. Shadowgraphs, Schlieren images, and the systems used to produce them share many similarities. However, the main physical difference between the two systems is that Shadowgraph systems do not use a light block, whereas Schlieren systems do. The images produced by a shadowgraph allow for the visualization of the change in the second derivative of density, however the addition of the light block enables the now Schlieren system to 45 visualize changes in the first derivative of density[29]. Following the addition of the light block, the first true Schlieren images were captured. The final iteration of the system can be seen in Figure 16 below. Figure 16: Mounting platform for camera and light source. © Brett Cosco CC BY-NC-ND 4.0. One significant observation made was that when this iteration was set up and properly aligned, the mirror appeared other worldly. The way the mirror looked was completely unexpected. When the mirror is observed outside of the Schlieren system, it works just as one would imagine. It reflected the object producing an image mildly distorted due to the curvature. When viewed through the system, strange phenomena were observed. First, the mirror does not seem to be reflective the way a normal mirror is, instead the mirror produces a neutral background in the color of the light source. The neutral background shows no traces of what the mirror is pointed at, it is as though there is no image. Second, when the images captured are examined more closely, strange textures, and abnormalities become apparent [31], [32]. The textures seem to be in the mirror; however, they cannot be identified on the mirror outside of the setup. There is a highly persistent example of one of these abnormalities at the center of the mirror that appears as a ring-like shape. The final observation is that when good alignment is achieved, minute disturbances in the environment begin to show up in the image. They appear as small swirls or waves, similar to how a gentle breeze would cause a hanging 46 sheet to move. With the experimental setup finalized, evaluation of the application of Schlieren imaging for the purpose of leak localization in CMVBs commenced. Three schematics of the Schlieren system, showing the top, side and three-dimensional views are provided in the following figures. Figure 17: Schematic showing side view of the system. © Brett Cosco CC BY-NC-ND 4.0. Figure 18: Schematic showing the top view of the system. © Brett Cosco CC BY-NC-ND 4.0. 47 Figure 19: Schematic showing three-dimensional view of system. © Brett Cosco CC BY-NC-ND 4.0. 3.3 Experimentation In order to gain an understanding of the capabilities and images produced by this system, various objects were imaged prior to testing leakages. The objects investigated in preliminary trials can be classified into three categories including thermal, pressure and density. Objects selected for testing include candles, compressed carbon dioxide (CO2), isopropyl alcohol, isobutane, lighters, sounds, and body parts. The candles and body parts belong to the thermal category, which means the effects seen can be attributed to density variation caused by temperature. Sounds belong to the pressure category, meaning the pressure change caused by sound is caused by the pressure change created by the sound. The final category consists of liquids and gasses with different densities than that of air, including compressed CO2, isopropyl alcohol, isobutane, and lighters. The thermal category allows for longer duration tests because there is a constant source of density variation. For instance, the candle can be left in the test area to 48 produce videos for as long as desired. The images produced with the candle vary, sometimes showing long laminar flow from the flame travelling up, and other times producing turbulent flows filled with swirls. Examples of both instances can be seen below. When visualizing thermal objects such as a hand, the effect is much more subtle. For instance, the images of a hand, held still, in the test area show small waves gently radiating from the hand. If the hand is waved, or two hands are rubbed together, more dramatic effects are produced. The pressure category consisting of sound, produces the most vivid images when paired with an additional source of density variation such as a candle. Sound on its own does produce enough variation to visualize however it is subtle. The wide variation of frequencies used in music makes it a difficult type of sound to use for imaging, however test tones for tuning audio equipment work exceptionally well. The single frequency tones make it far easier to recognize the effect in a Schlieren image. On a similar note, Schlieren imaging can be used to visualize standing sound waves[41]. Images of sound waves as the only density modifier did not provide the desired effect, and for this reason they were not included, however are worth mentioning as a possible density modifier. The density category generally produces vivid yet short duration images. The phenomenon is short lived because it relies on the interaction of the fluid added to the test area with the air present in the environment. Gasses such as air duster and isobutane are the shortest duration, and liquids such as isopropyl alcohol tend to linger for far longer. With insight gained from the preliminary trials, leakages could begin to be introduced. The first leakage experiment was as follows. The mirror was set up on a test vacuum 49 bag, consisting of a small aluminum plate, one layer of breather material, sealant tape to affix the vacuum bag, and the vacuum bag itself. The bag had one vacuum port for the evacuation of air. A hole was made in the vacuum bag directly in front of the mirror. The hole was made using an 18-gauge needle. Due to the small size of the camera screen, it is difficult to see small details in the image therefore evaluation must be done later, on a larger viewing screen. Upon review of the video files, it was noted that leakages alone do not produce significant density variation on the outside of the vacuum bag to be seen with my system. This does not mean that Schlieren imaging is an unacceptable method of leakage detection. Instead, it indicates that due to the physical specifications of the system, the sensitivity to density variation is below that which a leakage produces. With insight gained from early experimentation without leakages, methods of artificially introducing density variation and improving the visibility of the leakage were explored. The density modifiers selected include those from the density and thermal category. Each modifier was individually tested. Review of the initial leakage tests demonstrated the difficulty of viewing a leakage on the surface of the bag. When viewing Schlieren videos and images, it is difficult to understand what one is viewing without the context of being able to physically see the setup. In Schlieren videos, only shapes can be identified because they appear as silhouettes. When a leakage is made on the vacuum bag there is no point of reference outside of the system. However, there are points of reference that can be seen in the videos, but they are a result of imperfections in the mirror finish caused by the mounts in the telescope from which it came. One solution to address this and improve the likelihood of viewing a leakage is to introduce the leakage 50 and leave the needle inserted. Hypodermic needles were initially selected to make leakages because they offer standardized sizes. They also have a plastic opening at one end to attach to syringe that is a larger diameter than the main needle section. When the needles are left inserted, the plastic end is easily identifiable in the videos and provides a clear reference point for where to look for flow caused by a leakage. Since the opening is larger in diameter it will allow for a more concentrated flow to be observed, as opposed to when the leak is on the surface and the flow is multidirectional from around the leakage. The data was collected in three tests. The first test was the control, it made use of the density modifiers however there was no leakage present in the vacuum bag. The second test included leakages with the needle left in place, as well as the density modifiers. The third test used the density modifiers with the needle removed from the leakage. 3.3.1 Alignment Prior to evaluating leakages, the Schlieren system must be aligned. The process of aligning the system is outlined below. Many resources that outline the alignment of a schlieren system were consulted[30]–[32], [38]. It is worth noting that alignment is particular to the system and will depend on the equipment used. Due to the independent nature of the components in the experimental set up, alignment of the system requires many adjustments which could be lessened by utilizing an optics table or eliminated altogether using a commercially available Schlieren imaging system. The multi-step procedure used to align my system is outlined below. 51 First, the mirror is placed near the area of interest on the vacuum bag. Second, the tripod is placed roughly 2 m away from the mirror. Third, the base plate, which holds the camera, light source and razor blade, is affixed to the tripod before being leveled to ensure that the system will be as close to perpendicular as possible. Fourth, the lights in the room are turned off allowing for easier viewing of the image. Fifth, a large white surface showing the image from the mirror is used for coarse alignment in getting the light to travel close to the camera. Coarse alignment often involved picking up the tripod and moving it to better positions. Occasionally the tripod was intentionally set up at an incorrect distance to make alignment less difficult. This is due to the fact that when not at the recommended 2 times the focal length, the image of the light source is larger and easier to quickly identify. Sixth, once light is travelling close to the camera, a sheet of paper is used to get the light to hit either the camera body or lens itself. This also requires the tripod to be moved, however it is not completely picked up like in coarse adjustment. Instead, it is slid or walked on the ground. Seventh, the lens cap is placed over the lens and the fine adjustments begin. The raised NIKON logo on the lens cap works as a reference. The goal of the fine adjustment is to get the light source to appear as a pinpoint of light at the center of the K in NIKON. Eighth, the lens cap is removed, and alignment can be evaluated through the camera. It is possible to observe the alignment through the viewfinder, however since the system is so susceptible to disturbances, locking the mirror up and using the live view on the camera screen is far less prone to misalignments. Ninth, the image seen in the camera informs the process of fine tuning the alignment. If lens flare is present, it is an indicator that the mirror plane and camera lens plane are not parallel. If a bright object is present but not circular, the 52 light is not aimed directly at the mirror. Turning on the room lights helps to see the mirror as a mirror and the area of illumination at the same time. When performing fine adjustments, it is important to take into consideration the fact that when working with a mirror, adjustments are mirrored. Small adjustments to the overall tripod position may need to be made, but generally, the adjustability afforded by the light source rail system is enough to get the system aligned. Tenth, a candle is placed in the test area of the system. Further small adjustments may need to be made, but the camera should capture images resembling Schlieren images. Eleventh, with the system capturing shadowgraphs, the next step is adding the light block which changes the data being recorded from shadowgraphs to true Schlieren images. At this point, the end of the lens that is not attached to the camera body will be a few cm behind the location where the light from the mirror is focused to a perfect point. It is important that the lens is behind this location because the razor blade must be placed precisely at this location in such a way that it cuts off or blocks half of the point of light from entering the lens. This is not always possible and therefore it is far easier to observe the candle through the camera while testing different blade positions until the desired level of sensitivity is present in the image. Once the blade is set it can be beneficial to remove the candle and recheck the system with different subjects such as a hand, in case the blade is blocking too much light for the desired subject. Since the density variation caused by a candle is quite large, the blade may be placed to optimize visualization of that density gradient but may block too much or too little light from a subject that has a smaller density gradient because the light will not refract as much. 53 Once the system is aligned the camera settings must be adjusted. It is best to use manual mode because it affords full control over the camera settings, whereas the auto settings will try to compensate for the darkness and the mirror will be exposed incorrectly. The videos are captured at 30 frames per second (fps), meaning the slowest shutter speed was 1/30th of a second. In order to better capture the movements in the videos a shutter speed of 1/80th of a second was selected. The widest aperture possible was used to allow the maximum amount of light into the sensor, for the lens selected it was f/5.6. The ISO sensitivity was set to 1000. Each of the three settings, ISO, aperture and shutter speed work together to control the amount of light captured by the sensor. The shutter speed determines how long the shutter remains open, the faster the shutter speed, the less time the shutter is open. In addition to allowing less light into the sensor, fast shutter speeds also do a better job recording fast movement. The aperture controls the physical area that the light must pass through. The lower the f number, the wider the opening for the light. The aperture influences the appearance of the image or video where low aperture values such as f/1.4 produce shallow depth of field and high aperture values such as f/22 result in a very wide depth of field. Depth of field is the area that the subject appears suitably sharp. Finally, the ISO controls how sensitive the sensor is to light and is akin to selecting the appropriate film speed for the given lighting conditions. Increasing the ISO number makes the sensor more sensitive to light, however it comes at a cost. Shooting at higher ISO speeds like ISO 6400 can result in grainy videos. The settings of 1/80th of a second, f/5.6 and ISO 1000 provide a sufficiently bright video without being overly grainy. 54 Following the completion of the alignment procedure the system is ready for data collection. For the control test, there was no leakage in the vacuum bag. Instead, the goal was to investigate how the density modifiers behaved in a leak free environment. The second and third test both had leakages in the vacuum bag. Since the mirror was positioned on the surface of the vacuum bag and it was aligned under no leakage conditions, as soon as a leakage was introduced into the vacuum bag the pressure changed and the breather material became less compressed. This resulted in a small change in the mirror’s orientation, which meant realignment of the camera was needed before the second set of experiments could be conducted. The change in leakage size between the needle in the vacuum bag, and the leakage in the vacuum bag also caused the system to fall out of alignment. 3.3.2 Data Collection The data was collected under three conditions, no leakage, needle in place, and no needle leakage. The first test condition showed what the Schlieren system visualizes with no leakages, as well as, how density modifiers behave when no leakages are present. The procedure for all three conditions assumes that the system is aligned following the alignment procedure above prior to commencing data collection or condition changes. The first step is to begin recording and approach the test area. Step 2 is to pause when near the system to allow for any disturbances or air currents created by movement to dissipate. Step 3 is to introduce the density modifier to the test area. Step 4 is to wait for 10 seconds for the density modifier to finish interacting with the environment. The process is then repeated with the next density modifier used. 55 Having discussed the particularities of the experimental set up, the iterations of the system, and the process of alignment and data collection implemented in my research, the results of the experiments are presented in the following chapter. 56 4 Results This chapter highlights the key findings of the research and endeavors to present qualitative video-based data meaningfully for a text-based publication. The video data recorded during the experiments was captured at 30 fps, meaning one second of video data produces 30 individual frames, or images. It would be unfeasible to include all video data as individual frames, as a result, the raw video data was first trimmed into short duration videos highlighting each density modifier’s behavior. Once the videos were trimmed, each frame was extracted and saved as an image. Certain figures in this chapter are composed of the individual frames extracted from the trimmed video data. These figures show a frame-by-frame representation of the video data and span multiple pages. Note that the page number in the List of Figures corresponds to the location of the final frames in the sequence. The quality of the video captured is important when the videos were later analyzed. Still images taken from the videos captured by the three imaging devices highlight the improvements in video quality made through the iterations of the experimental set up. The three uncropped stills can be seen below. 57 Figure 20: Uncropped Schlieren Image taken with a smartphone camera. Figure 21: Uncropped Schlieren Image taken with a Nikon 1 J1. 58 Figure 22: Uncropped Schlieren Image taken with a Nikon D7100. From the preceding figures the quality of image provided by the D7100 is far superior to that of the 1 J1 and smartphone. The D7100 captures fine detail in the images that can be cropped for closer analysis. An example of a cropped 1 J1 versus a cropped D7100 image can be seen in Figure 14 and Figure 15 in Section 3.2. 4.1 No Leakage In order to establish a control, the density modifiers must first be imaged with the system in the absence of a leak. The density modifiers imaged include alcohol, butane, heat, CO2, and isobutane. Still image sequences captured from the videos of the experiments are presented below. 59 The first test was alcohol vapor from the bottle. Figure 23 below shows a bottle of isopropyl alcohol being squeezed near the test area causing vapor to flow into the test area. 60 Figure 23: Frame by frame sequence showing alcohol vapor being introduced to the test area by squeezing the bottle during the no leakage test. The next test also used alcohol as the density modifier. However, the alcohol was poured into the lid of the bottle and then placed in the test area. The still image sequence can be seen below. 61 Figure 24: Frame by frame sequence of alcohol vapor flowing from an open container during the no leakage test. The next density modifier tested is butane from a lighter. For this test the lighter is brought into the test area and the button is depressed allowing butane to flow. Images illustrating this test can be seen in the following figure. 62 Figure 25: Frame by frame sequence showing butane gas from a lighter being introduced to the test area during the no leakage test. 63 The third density modifier was heat. In this test a lit candle was placed in the test area in order to visualize the density change caused by the heat generated during the combustion process. Three placements of the candle are shown including center, right of center, and left of center. Figure 26: Frame by frame sequence showing the thermal plume of a candle centered in the test area during the no leakage test. 64 The candle to the right of center can be seen in the following image sequence. Figure 27: Frame by frame sequence showing the thermal plume of a candle on the right side of the test area during the no leakage test. 65 The candle to the left of center is the final test in this group. The still images from this test can be seen below. Figure 28: Frame by frame sequence showing the thermal plume of a candle on the left side of the test area during the no leakage test. 66 The next density modifier was CO2 gas. For this test a balloon was filled with CO2 gas, then the balloon was brought into the test area and the gas was released. 67 Figure 29: Frame by frame sequence showing CO2 being released into the test area from a balloon during the no leakage test. The next test also used CO2 gas, however in this test it was directly from the compressed gas cylinder. The valve of the tank was placed near the test area and the compressed gas was released; the image sequence can be seen in Figure 30 below. 68 Figure 30: Frame by frame sequence showing CO2 being released into the test area from a compressed gas cylinder during the no leakage test. The next test shows a candle being extinguished in the test area. 69 Figure 31: Frame by frame sequence showing a burning candle being extinguished during the no leakage test. 70 The following test demonstrates how the residual heat from an extinguished candle interacts with the air in the test area. 71 Figure 32: Frame by frame sequence showing the flow of air warmed by the residual heat from an extinguished candle during the no leakage test. The final test in the no leak control group is isobutane gas. The gas is sprayed into the test area from a valve on the cylinder. 72 Figure 33: Frame by frame sequence showing Isobutane being released into the test area from a compressed gas cylinder during the no leakage test. 73 4.2 Needle in Place This section presents the data collected in the needle in place experiment. For this test an 18-gauge needle was used to make a hole in the vacuum bag. The needle was left in place to provide a funnel like shape for the first leakage test. The first density modifier used in this experiment was the alcohol. The first alcohol test used the bottle to blow alcohol vapor into the test area. 74 Figure 34: Frame by frame sequence showing alcohol vapor from the bottle being drawn into the needle during the needle in place test. 75 The next alcohol test used the lid filled with alcohol placed beside the leakage. Figure 35: Frame by frame sequence showing alcohol vapor flowing from an open container into the needle during the needle in place test. 76 The next density modifier was butane from a lighter. The still images captured from the video data can be seen below. 77 Figure 36: Frame by frame sequence showing butane gas from a lighter being drawn into the needle during the needle in place test. 78 The next test used body heat as the density modifier. A hand was placed in the test area near the leak. 79 Figure 37: Frame by frame sequence showing air warmed by heat from a hand being drawn into the needle during the needle in place test. The next heat-based test used a lit candle placed in the test area as the source of heat. 80 81 Figure 38: Frame by frame sequence showing the thermal plume of a burning candle being drawn into the needle during the needle in place test. The next density modifier used was CO2. CO2 was placed in a balloon and was released into the test area. 82 83 Figure 39: Frame by frame sequence showing CO2 being released from a balloon and drawn into the needle during the needle in place test. 84 The next CO2 test poured the CO2 gas from a container into the test area. 85 Figure 40: Frame by frame sequence showing CO2 being poured from an open container and drawn into the needle during the needle in place test. 86 In the next test compressed CO2 gas is released into the test area from the cylinder. 87 Figure 41: Frame by frame sequence showing CO2 being released from a compressed gas cylinder and then be drawn into the needle during the needle in place test. The compressed CO2 test was conducted once more but with the CO2 being released from above the test area. 88 89 Figure 42: Frame by frame sequence showing CO2 being released from a compressed gas cylinder above the test area during the needle in place test. The next test uses isobutane as the density modifier. The isobutane is released into the test area by way of a valve on the cylinder. 90 Figure 43: Frame by frame sequence showing Isobutane being released from a compressed gas cylinder during the needle in place test. 91 The next test uses the residual heat from an extinguished candle as the density modifier. The extinguished candle is placed in the test area adjacent to the leak. 92 Figure 44: Frame by frame sequence showing the air warmed by the residual heat from an extinguished candle being drawn into the needle during the needle in place test. 93 4.3 The Leakage Experiment In this experiment the 18-gauge needle used to make the leakage is removed prior to the density modifiers being added. The first test in this experiment was the alcohol vapour from the bottle. The still images from this video data set can be seen in the following. 94 Figure 45: Frame by frame sequence showing alcohol vapor from the bottle being introduced to the test area during the leakage (needle removed) test. 95 In the next test the lid filled with alcohol was placed in the test area. 96 Figure 46: Frame by frame sequence showing alcohol vapor flowing from an open container into the leakage during the leakage (needle removed) test. Butane from a lighter was the next density modifier in this set of experiments. The still images detailing the visualization can be seen below. 97 Figure 47: Frame by frame sequence showing butane gas from a lighter being drawn into the leakage during the leakage (needle removed) test. 98 The next test is the heat-based test using a lit candle as the heat source. 99 Figure 48: Frame by frame sequence showing the thermal plume of a burning candle placed to the left of the leakage being drawn into the leakage during the leakage (needle removed) test. The test was repeated with the candle to the right-hand side of the leakage. 100 Figure 49: Frame by frame sequence showing the thermal plume of a burning candle placed to the right of the leakage being drawn into the leakage during the leakage (needle removed) test. 101 In the next test the candle was extinguished. The lit candle was placed to the side of the leak and was then extinguished. The residual heat in the candle provided the density modification in this test. 102 Figure 50: Frame by frame sequence showing the air warmed by the residual heat from an extinguished candle being drawn into the leakage during the leakage (needle removed) test. 103 The next density modifier used is CO2. For this test CO2 from a balloon was released into the test area. 104 Figure 51: Frame by frame sequence showing CO2 being released from a balloon and drawn into the leakage during the leakage (needle removed) test. 105 The CO2 balloon test was repeated from a position above the test area. 106 Figure 52: Frame by frame sequence showing CO2 being released from a balloon above the test area during the leakage (needle removed) test. Compressed CO2 was the next test subject in this set of experiments. For this test the CO2 was released from the cylinder into the test area. 107 Figure 53: Frame by frame sequence showing CO2 being released from a compressed gas cylinder during the leakage (needle removed) test. 108 CO2 was again used as a density modifier. For this test however, the CO2 was poured from a container into the test area. This was done twice, once from the left and once from above the test area. The first sequence of images is from the left. 109 Figure 54: Frame by frame sequence showing CO2 being poured from an open container and drawn into the leakage during the leakage (needle removed) test. 110 The next test is the same as the previous except the CO2 is released from above the test area. 111 Figure 55: Frame by frame sequence showing CO2 being poured from an open container above the test area and drawn into the leakage during the leakage (needle removed) test. 112 The final test in this experiment group uses isobutane as the density modifier. The isobutane is introduced from the left side of the test area. 113 Figure 56: Frame by frame sequence showing Isobutane being released from a compressed gas cylinder and drawn into the leakage during the leakage (needle removed) test. The previous image samples represent roughly one second of time each. The size of the images makes it difficult to identify what is taking place with respect to leakages. However, at this size it is easier to see the progression through time from frame to frame. In the following section selected frames showing the appearance of a leakage will be highlighted and discussed. 4.4 Analysis The sequential images from the video data presented in the previous sections give an idea of how the video data would appear, however it is difficult to see if Schlieren 114 imaging is able to visualize leakages. In this section selected frames from the video data will be presented which focus on leakages in Schlieren images. The first images with the needle in place provide reassurance that progress was being made as well as serving to identify the exact location of the leakage for when the needle is removed. In Figure 62 and Figure 65 below, there are two obvious imperfections on the mirror that appear as black spots (circled in red for easier identification), these can be used as reference points when the data is analyzed later as the mirror was not moved between tests. The first image is the needle with alcohol. The vapor can clearly be seen being drawn into the needle forming a funnel shape. Figure 57: Cropped image of alcohol vapor being pulled into the needle. The following three cropped frames show butane being drawn into the needle. Figure 58: Cropped frame by frame sequence demonstrating butane flowing into the needle. 115 Notice in Figure 58, above, the dark line seen in all three frames being pulled into the leakage. Body heat being drawn into the needle provides a less dramatic effect; however, it is still possible to identify the leak based on how the leak interacts with the density modifier in the test area. A cropped example of one of the body heat images can be seen below. Figure 59: Cropped image illustrating the flow caused by a leakage using body heat as the density modifier. In the above figure a light arc can be seen being curved into the opening of the needle, this is caused by the vacuum pulling the body heat into the needle. The candle on the other hand has the potential to produce more easily identifiable results as highlighted in Figure 60. Figure 60: Cropped image showing the thermal plume of a candle being drawn into needle. 116 The compressed CO2 proved difficult to visualize at times. Perhaps the gas was moving too quickly to be properly filmed at the given shutter speed. Regardless of the difficulty it was still possible to find an instance of compressed CO2 being pulled into the needle. Figure 61: Cropped image of compressed CO2 entering the needle. The CO2 being released from a balloon was also one of the more difficult density modifiers to use. Despite the difficulty, it produced indication of the leak, as seen in the following figure. Figure 62: Cropped image showing CO2 released from a balloon being drawn into the needle (mirror defects that were used as reference points circled in red). The darker region to the left of the needle opening is some of the swirling CO2 being pulled into the needle. The last CO2 example with the needle, is from the test where CO2 was poured from a container into the test area. Like all previous CO2 experiments it was difficult to select an individual image that adequately highlighted a leakage. 117 Figure 63: Cropped image showing faint flow of CO2 into the needle. Although faint, a darker region curving into the needle can be seen in the above figure. This is caused by the vacuum at the opening of the needle. Isobutane showing a leak can be seen in Figure 64 below. Figure 64: Cropped image showing isobutane flowing into the needle. Like previous examples seen, the isobutane shows the characteristic dark region at the opening of the needle. The final density modifier is the residual heat from the candle. This density modifier did quite well at visually showing a leak. The following image is one of the most indicative of a leak compared with the other needle experiments. It also shows where the needle enters the vacuum bag in relation to the defects in the mirror, this will provide a means of confirming that what is being identified as leakages are in fact in the leakage location. 118 Figure 65: Cropped image of air, warmed by the residual heat of a candle, being pulled into the needle (mirror defects circled in red). The needle in place experiments provided evidence in support of the application of Schlieren imaging for the purpose of leakage detection. The no needle leakage experiments were also successful in terms of visually identifying leakages. Selected frames will be presented below detailing what the density modifiers show in terms of leakages. Take note of the location of the black spots circled in Figure 62 and Figure 65 in relation to where the needle enters the vacuum bag, as this is important for identifying if the flow towards the point in the bag is the actual leakage location. The dark spots are visible in most frames, however on occasion a dark region will fall in line with the dark spots and will become difficult to see for a frame or two, however they return. Figure 66: Cropped image showing alcohol vapor being drawn into the leakage. In the above figure, alcohol vapor from the lid of the bottle is being pulled towards the leak as it evaporates. The next figure is a collection of six successive frames from the 119 butane experiment. In the six frames, a persistent dark region that comes to a point at the dark boundary at the bottom of the frame where the leak is located. Figure 67: Cropped frame by frame sequence showing butane being drawn into the leakage. The above sequence of images shows what to expect when a leak is viewed in the video data. When the last frame is viewed on its own it would be difficult to pinpoint the leakage, however when viewed in context with other images, the leakage location becomes more apparent. Similarly, when viewing the videos, it is much easier to identify a leakage than when dealing exclusively with still images. As seen in the needle experiments the candle heat provided excellent results and the same was true for the no needle experiment. In Figure 68 warm air from the candle can be seen being pulled towards a point. 120 Figure 68: Cropped image showing the thermal plume of a candle being pulled into the leak. The compressed CO2 test produced better results without the needle, perhaps due to the needle being too directional. Compressed CO2 being pulled towards the leak can be seen in the figure below. Figure 69: Cropped images showing compressed CO2 being pulled into the leak. It is difficult to see, however there is a light region that appears at the bottom center of the frame. It remains in the same spot across two frames and is caused by the leak pulling CO2 into the vacuum bag. The CO2 released from the balloon also successfully visualized leaks. The figure highlighting this can be seen below. 121 Figure 70: Cropped images illustrating CO2 flowing into the leak. Note how the lines converge to a point at the bottom center of the image, above the leak. Pouring the CO2 from a container produced adequate results which can be seen in Figure 71. Figure 71: Cropped images showing poured CO2 being pulled into the leak. Once again lines and shapes can be seen converging to a point near the bottom center of the image. The isobutane provided satisfactory results. The figure below faintly shows a streak or dark region pulled towards a point at the bottom center of the image. 122 Figure 72: Cropped image of isobutane entering the leak. Although faint, when viewed contextually, it is more apparent that there is a leakage present. Much like the last frame in Figure 67 above, when there is more than one frame it is evident that a leak is being viewed. The final test was heat based and used the residual heat from an extinguished candle. The visualization of a leak using the extinguished candle can be seen in Figure 73. Figure 73: Cropped image showing residual heat from a candle enter the leakage. The warm air is being drawn from the candle to the source of the leakage on the vacuum bag. Examining the figures presented in this section it is clear that the Schlieren system is visualizing leakages in the presence of density modifiers. All figures presented for closer analysis indicate convergence to a point at the bottom center of the frame where the needle was located in the needle in place experiments as determined 123 from the reference points. An example of this confirmation can be seen in the following figure. Figure 74: Confirmation of leakage location using reference points and needle. Needle in place as background image, with no needle image overlayed at 50% opacity with reference points aligned(left) and no needle as background image with needle in place overlayed at 50% opacity with reference points aligned(right) Magenta is the background color and helps indicate which image is at reduced opacity. When the same images are viewed in their uncropped form the point of convergence is located in the middle of a flat spot at the bottom of the mirror which is the same location as the leakage. Despite all the density modifiers being used to successfully identify a leakage in Schlieren images, some work better than others. Take for instance butane. It was one of the best performing density modifiers from the density category, however it comes with practical concerns regarding its flammability. On the other hand, there is CO2. It is not one of the best density modifiers, however it is not overly harmful in small quantities making it suitable for use in many environments. Heat performed similarly if not better than butane for visualizing leaks. The nature of heat-based density modifiers means that longer test can be conducted. This is one of the reasons that makes it one of the most effective density modifiers. The lit candle worked quite well, but the open flame is not practical. The residual heat from the candle provided the clearest indication of the location of the leakage, as seen in Figure 73 above. 124 The goal of this research, to explore the applicability of Schlieren imaging for the purpose of leakage localization, stemmed from the need for a method of definitively locating leakages in CMVBs that could be incorporated into the framework developed by Esmaeili et al. The findings presented in this chapter serve as a proof of concept that Schlieren imaging has potential to become a useful tool for locating leakages in vacuum bags. 125 5 Conclusions and Recommendations The research that forms the basis of this thesis was motivated by lack of definitive localization techniques for CMVB processes. The process of proposing a novel leak localization technique began with an analysis of currently used methods. This included the method proposed by Esmeaili et al. that uses machine learning to make location predictions based on flowrate data. The analysis identified a need for a technology that would allow for the exact location of leakages to be determined. Upon consideration of how one might identify a leakage with certainty, it became apparent that the problem would become much easier if one could simply visualize the leak in a similar manner to how water leaks from a container. This idea led to the field of Schlieren imaging, a technique used to visualize small variations in the density of transparent media. The theory behind Schlieren imaging as well as its uses were explored. Though a thorough search of the literature pertaining to Schlieren imaging returned no support for its use in leakage localization in CMVBs, it provided information needed to construct a Schlieren system for experimentation. The final iteration of the experimental set up used to capture the images presented in Chapter 4, proved that Schlieren imaging has the potential to be used as a method for leakage localization in CMVBs. I hope that the figures presented here make a compelling case for the future research and development of Schlieren imaging as a means of leakage localization in CMVB processes. 126 5.1 Limitations of the Research The research presented in this thesis is a proof of concept for Schlieren imaging for leak localization. This section highlights the limitations of the research conducted and places less emphasis on Schlieren imaging with respect to leak localization. Since the concept of Schlieren imaging for leakage localization is in its infancy, further research is required before definitive conclusions can be drawn. In Chapter 3 the iterations of the Schlieren system are presented alongside the images they captured. Based on the progression of quality seen in the images through each iteration of the system, as the individual components were replaced by better quality or more suitable alternatives, clearer, more identifiable results were seen in the images. Evidently, quality is one of the most important aspects of the components of the system. The improvement in the image quality achieved with the substitution of the scientific demonstration mirror, for a telescope primary mirror, and the 1J 1 mirrorless camera for the D7100 DSLR are most illustrative of this. However, this increase in component quality comes with a significant cost. The limited equipment budget was the fundamental limitation of the research. The impact budget had on the research will be analyzed, and potential solutions will be presented in the following. The system used to collect the data is composed of different components, each placed independently of one another. When placed and aligned accurately, Schlieren images are produced, however independently aligning each component is time consuming, especially because there is no common frame of reference. The Schlieren system developed was capable of visualizing the leaks in the vacuum bag, which is what the 127 research endeavored to answer. However, the Schlieren system used was not able to visualize leakages without the aid of one of the density modifiers. This could be caused by any number of factors; however, the most plausible explanation follows. Firstly, the focal length of the mirror may be too short to allow the light enough time to refract before entering the camera. Secondly, the small vacuum pump available for testing may not have been able to apply a strong enough vacuum. Both issues likely could have been addressed, however due to the limited budget available and time constraints, density modifiers were used. In researching components for my Schlieren system I encountered many products that would have been beneficial to my system however due to time or budget they were not able to be used. These include system components such as high-speed cameras, different focal length mirrors, as well as off the shelf systems capable of capturing schlieren images[33], [34], [36], [42]–[46]. For instance, the Schlieren system with the same mirror specifications as my experimental set up has a price of $ 7 184.00, and ships from India[42]. One of the most exciting aspects of this research was the quality of the data recorded. High quality images showing the exact location of leakages were captured with a Schlieren system predominantly constructed with everyday objects, except for the mirror, which required a specialty optics store to procure. The camera, light source, razor blade, and tripod were easily obtainable. Additionally, this technique did not require extensive training or expertise, outside of the construction of the system to successfully identify leaks. Another key limitation of the research can be attributed to the general scarcity of information on Schlieren imaging for this application. After an extensive review of the 128 literature, it was determined that there is no literary starting point, no “How to build a sturdy high sensitivity Schlieren system”. At one end of the spectrum there are the how to set up a Schlieren system for science demonstrations or personal interest, which were exceptionally helpful in early iterations which led to the recommendation of telescope mirrors [23], [24], [31], [32]. On the other end of the spectrum there are resources from academia, which focus more on what Schlieren imaging was used to accomplish[28], [47], rather than how to assemble a system or the theory behind Schlieren imaging. The process of researching how to assemble a system did return several results for commercially available Schlieren systems and laboratories that had Schlieren systems, that would be more adept at visualizing a leakage than the system used for this research[42], [43], [48]. Unfortunately, the cost was prohibitive, and the niche nature of such equipment made it inaccessible, however this could be an avenue worth exploring. The final key limitation of the research was space constraints. There was nowhere to set the system up and leave it for an extended period in the lab to test repeatedly. This resulted in constant setup and take down of the system. Additionally, the lab was for composite research thus it had no optics equipment, aside from the DSLR, which was used to photograph composite samples. A small workshop with a drill press and a vertical band saw among other disused pieces of equipment was attached to the lab. It did not offer much in the way of materials to assist the construction of a setup, predominantly being storage for various fasteners and vacuum fittings. The workshop did allow for the creation of the mounting rail system which was constructed out of recycled plywood from a shipping crate, spare fasteners, threaded rod, and ring stand 129 fittings from the workshop. The rail system greatly reduced the difficulty of aligning the system but is the product of circumstance and ingenuity and leaves much room for improvement. Despite the success in visualizing leakages in vacuum bags with the Schlieren system further development is required for use of Schlieren imaging as a practical means of leakage localization on an industrial scale. 5.2 Conclusions As stated in Chapter 1, this research grew from the need for a method of leakage localization that could definitively locate a leakage. This guided the research towards Schlieren imaging however, an exhaustive search deemed it an uncommon topic with respect to vacuum bag leakage localization. In fact, there was no literature supporting this application of Schlieren imaging. Initially, the objective of the research was to determine if a leak could be visualized using a Schlieren system. Of course, this is not difficult to investigate, as it requires placing a vacuum bag with a known leak in the test area of a Schlieren system, and reviewing the videos or images captured. Unfortunately, I did not have the ability to purchase, nor did I have access to such a system. At this point, the research objective became two-fold. The main objective was to determine whether Schlieren imaging was capable of visualizing and, by extension, locating leakages. The development of my own Schlieren system became the secondary objective. From ideation to execution, the process followed a logical journey motivated by my own curiosity in the topic of Schlieren imaging. This self-guided approach culminated in the 130 creation of a functional Schlieren system which captured the images presented in Chapter 4. Examining the images, it is evident that the system is capable of visualizing leakages. I believe that the images captured can be used to draw conclusions with respect to the two-fold research objective previously stated. The first conclusion is that a functional Schlieren system capable of high-quality images can be constructed with common objects. The second, and most exciting conclusion drawn from the images produced is that Schlieren imaging can be used as a novel method for visualizing leakages in vacuum bags. These conclusions serve to open the door for future work in the field of Schlieren imaging for leakage localization vacuum bag-based CM. 5.3 Future Work The data collected supports the validity of using Schlieren imaging for leakage localization. It is noted that the research presented is at the proof-of-concept stage and further development work is required for a ready-to-use Schlieren system for locating leakages in industry. The research identified three main areas for future research. The first area worth exploring with respect to future development of Schlieren imaging as a viable leak localization technique is the development of a lightweight frame based Schlieren system that could be moved around a vacuum bag without requiring realignment. By adding the components of the system to a frame, a common frame of reference is created that would decrease initial alignment time and allow for the entire system to be moved to search the bag without needing major re-alignment. 131 A second area for future research is the Schlieren system itself. As stated in the limitations, the only viable mirror available had a focal length of around 1200 mm, perhaps a longer focal length mirror would allow vacuum bag leakages to be imaged without density modifiers. In addition to experimentation with focal lengths, there are more configurations of Schlieren imaging systems that can be tested[25], [26], [36], [47]. These include systems consisting of multiple mirrors as well as lens-based systems. Further experimentation with the components of the system is also worth considering. Take for example the light source. A proper pinhole aperture would likely offer much more adjustability than the aluminum foil pinhole aperture used for the experimental setup. The camera also presents a logical area for future experimentation. A Nikon D7100 DSLR was used in the system. While this does represent a consumer grade digital imaging device, there are far higher quality cameras available that may be better suited for Schlieren imaging. The final area identified for future work focuses on the implementation of Schlieren imaging in conjunction with other techniques. Once a lightweight portable Schlieren system has been realized, Schlieren imaging can be more easily implemented for leak localization. Much like the other methods of localization, it is not a one answer solution, and will be most valuable when used in conjunction with one of the other leak localization techniques. The initial implementation may be as simple as using the flowrate and machine learning based approach to decrease the search area from the entire bag down to 10% of the total bag area. The reduced search area would then be manually scanned using the Schlieren system until the leak is identified in the vacuum bag. This represents the 132 quickest and easiest means of implementing Schlieren imaging, however there is potential for automation. The Schlieren system could be added to an overhead gantry, which would allow for the vacuum bag to be searched without human interference. Taking it a step further, the automated Schlieren system could be integrated with a classification-based machine learning algorithm to identify the presence of leakages in the vacuum system. 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Thesis/Dissertation
eng
Mechanical Engineering
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Najjaran, Homayoun
Siddiqua, Sumi
A novel application of Schlieren imaging : leakage localization in composite manufacturing vacuum bags
Text