聚酰胺(PA9T)之雷射穿透焊接及其加工參數之機器學習分析

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姓名

賴俊霖(Chun-Lin Lai) 查詢紙本館藏

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光機電工程研究所

論文名稱

聚酰胺(PA9T)之雷射穿透焊接及其加工參數之機器學習分析
(Laser Transmission Welding of Polyamide 9T (PA9T) and Machine Learning Analysis of Its Processing Parameters)

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摘要(中)

由於低吸水率，高尺寸穩定性和快速結晶特性，聚酰胺9T在塑膠加工業的應用上大有前景，尤其是電子產品中的絕緣體和包裝材料。本研究旨在探討其雷射焊接特性與雷射焊接參數之間的關係。首先，我們通過實驗研究了四個雷射加工參數，雷射功率、掃描速度、雷射頻率以及重複焊接次數對焊縫尺寸和焊接強度的影響。焊縫尺寸主要由焊道的寬度和深度來表示，而強度則基於剝離測量。結果顯示，隨著雷射功率和焊接次數的增加，焊道的尺寸和強度均增大。但是，過多的雷射能量會導致焊縫汽化，結果觀察到焊接強度的下降。研究的第二部分是通過機器學習獲得焊接性能與加工參數之間的關係。結果顯示，雷射功率是影響焊接強度的最重要因素。

摘要(英)

Due to its low water absorption, high dimensional stability and fast crystallization, poly-amide 9T is a widely used plastic material, especially as an insulator or packaging material in electronics and optoelectronics. This study aims to explore the relationship between its laser welding characteristics and laser welding parameters. First, we conducted experiments to study the effects of four laser processing parameters: laser power, scanning speed, pulse frequency, and repetitive welding times on weld size and welding strength.
Weld size is mainly represented by the width and depth of the weld, while strength is based on the peeling measurements. Results showed that as laser power and number of repetitive welding times was increased, both weld size and strength were increased. However, excessive laser energy caused cause vaporization of the weld that degraded the polymer, and as a result, a decrease in weld strength was observed. The second part of this study is to obtain the relation-ship between the welding performance and the processing parameters through machine learning. The results showed that laser power was the most important factor affecting the strength of the weld.

關鍵字(中)

★ 雷射
★ 穿透焊接
★ PA9T

關鍵字(英)

論文目次

國立中央大學 i
中文摘要 iii
Abstract iv
Contents v
List of Figures viii
List of Tables xi
Chapter 1 Introduction 1
1-1 Plastics and Their Welding Technologies 1
1-2 Laser and Its Applications in Plastic Welding 2
1-3 Artificial neural network 3
1-4 Background and purpose of the study 6
Chapter 2 Literature review 7
2-1 Plastics 7
2-2 Welding 8
2-3 Laser welding 10
2-3-1 Development of laser welding 10
2-3-2 Laser welding technology 11
2-3-3 Laser transmission welding (LTW) 12
2-4 Artificial neural network 16
2-5 Summary 17
Chapter 3 Experimental details 19
3-1 Experiment procedure 19
3-2 Sample preparation 19
3-2-1 Materials 19
3-2-2 Laser setup 21
3-3 Characterizations 23
3-3-1 Welding width and welding depth 23
3-3-2 Peel off strength 24
3-4 Analysis 25
3-4-1 Artificial Neural network 25
3-4-2 Random forest [32] 32
3-4-3 Multiple regression analysis 37
3-5 Materials and equipment 38
3-5-1 Materials for experiments 38
3-5-2 Experimental apparatus 39
Chapter 4 Results and Discussion 40
4-1 Transmittance of PA9T 40
4-1-1 Temperature measurement 41
4-2 Characterizations of the weld 48
4-2-1 Welding width 49
4-2-2 Welding depth 58
4-2-3 Peel-off strength 67
4-3 MRA result 72
4-4 Random forest and ANN prediction result 74
Chapter 5 Conclusion 82
References 84
Attachment 87
碩士論文口試教授問題集 106

參考文獻

[1] M. Speka, S. Matteï, M. Pilloz, and M. Ilie, "The infrared thermography control of the laser welding of amorphous polymers," NDT & E International, vol. 41, no. 3, pp. 178-183, 2008.
[2] G. Zak, L. Mayboudi, M. Chen, P. J. Bates, and M. Birk, "Weld line transverse energy density distribution measurement in laser transmission welding of thermoplastics," Journal of Materials Processing Technology, vol. 210, no. 1, pp. 24-31, 2010.
[3] N. Panigrahi and S. Tripathy, "Application of Soft Computing Techniques to RADAR Pulse Compression," 07/15 2020.
[4] N. Yorek and I. Ugulu, "A CFBPN Artificial Neural Network Model for Educational Qualitative Data Analyses: Example of Students′ Attitudes Based on Kellerts′ Typologies," Educational Research and Reviews, vol. 10, no. 18, pp. 2606-2616, 2015.
[5] K. Mistry, "Tutorial plastics welding technology for industry," Assembly Automation, vol. 17, no. 3, pp. 196-200, 1997.
[6] P. Bates, J. MacDonald, C. Wang, J. Mah, and H. Liang, "Vibration welding nylon 66-Part I experimental study," Journal of Thermoplastic Composite Materials, vol. 16, no. 2, pp. 101-119, 2003.
[7] P. Bates, J. MacDonald, C. Wang, and H. Liang, "Vibration Welding Nylon 66—Part II Finite Element Analysis," Journal of Thermoplastic Composite Materials, vol. 16, no. 3, pp. 197-211, 2003.
[8] D. Ziegler, "Extrusion Welding of Thermoplastic Materials," TECHNICAL PAPERS-SOCIETY OF MANUFACTURING ENGINEERS-ALL SERIES-, 1999.
[9] R. Wise and I. Froment, "Microwave welding of thermoplastics," Journal of materials science, vol. 36, no. 24, pp. 5935-5954, 2001.
[10] H. Potente, O. Karger, and G. Fiegler, "Laser and microwave welding–the applicability of new process principles," Macromolecular Materials and Engineering, vol. 287, no. 11, pp. 734-744, 2002.
[11] W. Duley and R. Mueller, "CO2 laser welding of polymers," Polymer Engineering & Science, vol. 32, no. 9, pp. 582-585, 1992.
[12] B. Rooks, "Laser processing of plastics," Industrial Robot: An International Journal, vol. 31, no. 4, pp. 338-342, 2004.
[13] R. P. Martukanitz, "A critical review of laser beam welding," in Critical Review: Industrial Lasers and Applications, 2005, vol. 5706: International Society for Optics and Photonics, pp. 11-24.
[14] https://www.laserline.com/en-int/heat-conduction-welding/ (accessed.
[15] T. Zacharia, S. David, J. Vitek, and T. Debroy, "Weld pool development during GTA and laser beam welding of type 304 stainless steel part I. Theoretical analysis," Welding journal, vol. 68, no. 12, 1989.
[16] W. D. C. Bransch H. N., Kerr H. W. , "Effects of Pulse Shaping on NchYAG Spot Welds in Austenitic Stainless Steel " 1994. [Online]. Available: http://files.aws.org/wj/supplement/WJ_1994_06_s141.pdf.
[17] E. S. Ng and I. A. Watson, "Characteristics of Nd: YAG laser welded high carbon steels," Journal of laser applications, vol. 9, no. 5, pp. 243-252, 1997.
[18] H. Potente, J. Korte, and F. Becker, "Laser transmission welding of thermoplastics: analysis of the heating phase," Journal of reinforced plastics and composites, vol. 18, no. 10, pp. 914-920, 1999.
[19] . [Online]. Available: https://www.laserline.com/en-int/laser-plastic-welding/.
[20] V. Kagan, R. Bray, and W. Kuhn, "Laser transmission welding of semi-crystalline thermoplastics—Part I: Optical characterization of nylon based plastics," Journal of reinforced plastics and composites, vol. 21, no. 12, pp. 1101-1122, 2002.
[21] V. Kagan and G. Pinho, "Laser transmission welding of semicrystalline thermoplastics-Part II: Analysis of mechanical performance of welded nylon," Journal of reinforced plastics and composites, vol. 23, no. 1, pp. 95-107, 2004.
[22] R. Prabhakaran, M. Kontopoulou, G. Zak, P. Bates, and B. Baylis, "Contour laser–Laser-transmission welding of glass reinforced nylon 6," Journal of Thermoplastic Composite Materials, vol. 19, no. 4, pp. 427-439, 2006.
[23] H. Potente, G. Fiegler, H. Haferkamp, M. Fargas, A. Von Busse, and J. Bunte, "An approach to model the melt displacement and temperature profiles during the laser through‐transmission welding of thermoplastics," Polymer Engineering & Science, vol. 46, no. 11, pp. 1565-1575, 2006.
[24] C. Rüffler and K. Gürs, "Cutting and welding using a CO2 laser," Optics & Laser Technology, vol. 4, no. 6, pp. 265-269, 1972.
[25] S. Nielsen, "Laser material processing of polymers," Polymer testing, vol. 3, no. 4, pp. 303-310, 1983.
[26] P. A. Atanasov, "Laser welding of plastics: theory and experiments," Optical Engineering, vol. 34, no. 10, pp. 2976-2981, 1995.
[27] H. Luo, H. Zeng, L. Hu, X. Hu, and Z. Zhou, "Application of artificial neural network in laser welding defect diagnosis," Journal of Materials Processing Technology, vol. 170, no. 1-2, pp. 403-411, 2005.
[28] M.-J. Tsai, C.-H. Li, and C.-C. Chen, "Optimal laser-cutting parameters for QFN packages by utilizing artificial neural networks and genetic algorithm," Journal of Materials Processing Technology, vol. 208, no. 1-3, pp. 270-283, 2008.
[29] E. Kayabasi, S. Ozturk, E. Celik, and H. Kurt, "Determination of cutting parameters for silicon wafer with a Diamond Wire Saw using an artificial neural network," Solar Energy, vol. 149, pp. 285-293, 2017.
[30] C.-C. J. Lin, "A neural network-based methodology for generating spectrum-compatible earthquake accelerograms," University of Illinois at Urbana-Champaign, 2000.
[31] M. R. Azimi-Sadjadi, S. Sheedvash, and F. O. Trujillo, "Recursive dynamic node creation in multilayer neural networks," IEEE Transactions on Neural Networks, vol. 4, no. 2, pp. 242-256, 1993.
[32] L. Breiman, "Random forests," Machine learning, vol. 45, no. 1, pp. 5-32, 2001.
[33] C. Ding, X. Wu, G. Yu, and Y. Wang, "A gradient boosting logit model to investigate driver’s stop-or-run behavior at signalized intersections using high-resolution traffic data," Transportation research part C: emerging technologies, vol. 72, pp. 225-238, 2016.
[34] L. Cheng, X. Chen, J. De Vos, X. Lai, and F. Witlox, "Applying a random forest method approach to model travel mode choice behavior," Travel behaviour and society, vol. 14, pp. 1-10, 2019.
[35] Y. Amit and D. Geman, "Shape quantization and recognition with randomized trees," Neural computation, vol. 9, no. 7, pp. 1545-1588, 1997.
[36] F. G. Bachmann and U. A. Russek, "Laser welding of polymers using high-power diode lasers," in Laser Processing of Advanced Materials and Laser Microtechnologies, 2003, vol. 5121: International Society for Optics and Photonics, pp. 385-398.
[37] V. Kecman, Learning and soft computing: support vector machines, neural networks, and fuzzy logic models. MIT press, 2001.

指導教授

何正榮

審核日期

2020-8-19

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