Multi-objective Optimization and Control System Design for Quality Assurance

簡易檢索 / 詳目顯示

回結果列表

研究生：	GUNAWAN DEWANTORO GUNAWAN - DEWANTORO
論文名稱：	Multi-objective Optimization and Control System Design for Quality Assurance Multi-objective Optimization and Control System Design for Quality Assurance
指導教授：	郭中豐 Chung-Feng Jeffrey Kuo
口試委員:	黃昌群 Chang-Chiun Huang 張嘉德 Chia-Der Chang
學位類別：	碩士 Master
系所名稱：	工程學院 - 自動化及控制研究所 Graduate Institute of Automation and Control
論文出版年：	2011
畢業學年度：	99
語文別：	英文
論文頁數：	112
外文關鍵詞：	Cavity Pressure Control, Model Reference Adaptive System
相關次數：	點閱：441 下載：1
分享至:	分享至facebook 分享至twitter

查詢本校圖書館目錄查詢臺灣博碩士論文知識加值系統勘誤回報

Quality assurance is the process of verifying or determining whether products or services meet or exceed customer expectations. In this study, both multi-objective optimization and control system design were utilized for yielding specific steps to help define and attain goals. These quality-assuring approaches were applied to the light guide plate and scribed thin film. This study employed a procedure for solving the multi-objective cases in the Taguchi method utilizing two data envelopment analysis (DEA) approaches, including comparisons of efficiency between different systems (CEBDS) and bilateral comparisons. In the injection molding case, the quality characteristics, namely: residual stress, V-cut depth and angle, were improved. The reliability and reproducibility of the experiment were verified by confirming a confidence interval 95%. In the fiber laser scribing case, the total anticipated improvements of the quality characteristics, namely: upper line width, lower line width, and surface bump height, were investigated with respect to single-objective Taguchi method. The outcome showed better anticipated improvements than that of the Taguchi method which is only superior in improving one quality while sacrificing the other qualities. A cavity pressure control scheme was also proposed by applying Model-reference Adaptive System (MRAS) to deal with the time-varying nature of cavity pressure. The simulation demonstrated the effectiveness of MRAS-based control system during filling and packing phases even in the presence of parameter variations. Since cavity pressure is one of significant factor in injection molding machine, thus the proposed control scheme is potential to be implemented in the real system.

Abstract……………………………………………………………………………………i
Acknowledgement………………………………………………………………………… ii
Table of Contents…………………………………………………………………………iii
List of Figures……………………………………………………………………………vi
List of Tables……………………………………………………………………………ix
Introduction	1
1	Research Background	1
2	Research Objectives	4
3	Literature Review	5
4	Overview of Thesis	11
Basic Principle of Injection Molding and Fiber Laser Scribing	12
1	Components of the Injection Molding Process 	12
2	Phases of the Injection Molding Cycle	16
2.1	Injection/Filling Phase	16
2.2	Holding Pressure Phase	17
2.3	Cooling Phase	19
3	Laser Operation	21
4	Fiber Laser	22
5	Laser Scribing	24
Research Methodology	27
1	Materials and Equipments	28
1.1	Injection Molding	28
1.2	Fiber Laser Scribing	31
2	Taguchi Quality Method 	32
2.1	Orthogonal Array	33
2.2	Parameter Design	33
2.3	Main Effect Analysis	34
3	Back-Propagation Neural Network	35
4	Analysis of Variance	36
5	Confirmation Experiment	39
5.1	Confidence Interval of the Theoretically-Predicted Value	39
5.2	Confidence Interval of the Calculated Experiment Value	40
6	Data Envelopment Analysis (DEA)	40
6.1	Comparisons of Efficiency Between Different Systems (CEBDS)	42
6.2	Bilateral Comparisons	43
7	Model Reference Adaptive System (MRAS) 	43
Results and Discussion	47
1	Part I: Injection Molding	47
1.1	Taguchi Experimental Method	47
1.2	ANOVA Statistical Results	53
1.3	Multi-objective Optimization	56
1.4	Confirmation Experiments	61
2	Part II: Fiber Laser Scribing	68
2.1	Taguchi Experimental Method	68
2.2	Utilization of Back-Propagation Neural Network (BPNN)	71
2.3	ANOVA and Main Effect Analysis	73
2.4	Multi-objective Optimization	75
3	Part III: Cavity Pressure Control	78
3.1	Control Design	78
3.2	Simulation results	80
Conclusion	89
References………………………………………………………………………………90
Appendix 1 	The efficiency of all DMUs using CEBDS in injection molding case …….. 	94
Appendix 2 	The efficiency of all DMUs using bilateral comparisons approach in    injection molding case…...………………………………...……………….. 96
Appendix 3 	The efficiency of all DMUs using bilateral comparisons approach in fiber laser scribing case…………………………………………………………...	98
Appendix 4 	Simulink diagram for MRAS-based control for filling phase ...…………..	100
Appendix 5 	Simulink diagram for MRAS-based control for packing phase …………...	101

                                

1. Choi, D.S., and Im, Y.T., “Prediction of Shrinkage and Warpage in Consideration of Residual Stress in Integrated Simulation of Injection Molding,” Composite Structures, Vol. 47, No. 1-4, pp. 655-665 (1999).
2. Wu, C.H., and Chen, W.S., “Injection Molding and Injection Compression Molding of Three-beam Grating of DVD Pickup Lens,” Sensors and Actuators A: Physical, Vol. 125, No 2, pp. 367-375 (2006).
3. Thian, E.S., Loh, N.H., Khor, K.A., and Tor, S.B., “Microstructures and Mechanical Properties of Powder Injection Molded Ti-6Al-4V/HA Powder.” Biomaterials, Vol. 23, No. 14, pp. 2927–2938 (2002).
4. Liu, S.J., Lin, C.H., and Wu, Y.C., “Minimizing the Sinkmarks in Injection-molded Thermoplastics,” Advances in Polymer Technology, Vol. 20, No. 3, pp. 202-215 (2001).
5. Murakami, O., Kotaki, M., and Hamada, H., “Effect of Molecular Weight and Molding Conditions on the Replication of Injection Molding With Micro-Scale V-Groove Features," Polymer Engineering & Science, Vol. 48, No. 4, pp. 697-704 (2008).
6. Huang, M.S., Yu, J.C., and Lin, Y.Z., “Experimental Rapid Surface Heating by Induction for Micro-Injection Molding of Light-Guided Plates,” Journal of Applied Polymer Science, Vol. 113, No. 2, pp. 1345-1354 (2009).
7. Al-Refaie, A., “Optimizing SMT Performance Using Comparisons of Efficiency Between Different Systems Technique in DEA,” Electronics Packaging Manufacturing, IEEE Transactions on, Vol. 32, No. 4, pp. 256-264 (2009).
8. Mohan, S.V., Raghavulu, S.V., Mohanakrishna, G., Srikanth, S., and Sarma, P.N., “Optimization and Evaluation of Fermentative Hydrogen Production and Wastewater Treatment Processes using Data Enveloping Analysis (DEA) and Taguchi Design of Experimental (DOE) Methodology,” International Journal of Hydrogen Energy, Vol. 34, No.1, pp. 216-226 (2009)
9. Lior, N., “Energy Resources and Use: The Present (2008) Situation and Possible Sustainable Paths to the Future,” Energy, Vol. 35, No. 6, pp. 2631-2638 (2010)
10. Wang, R.Z., and Zhai, X.Q., ”Development of Solar Thermal Technologies in China,” Energy, Vol. 35, No. 11, pp. 4407-4416 (2010).
11. Preu, R., Biro, D., Rentsch, J., Clement, F., Erath, D., Emanuel, G., et al., “The Status of Silicon Solar Cell Production Technology Development at Fraunhofer ISE,” Photovoltaic Energy Conversion, Conference Record of the 2006 IEEE 4th World Conference on. Waikoloa, HI, USA, pp.1040-1043 (2006).
12. Wu, B.R., Wuu, D.S., Wan, M.S., Huang, W.H., Mao, H.Y., and Horng, .RH., Fabrication of nc-Si/c-Si Solar Cells using Hot-Wire Chemical Vapor Deposition and Laser Annealing,” Solar Energy Materials and Solar Cells, Vol. 93, No. 6-7, pp. 993-995 (2009).
13. Dobrzanski, L.A., Drygala, A., Golombek, K., Panek, P., Bielanska, E., and Zieba, P., Laser Surface Treatment of Multicrystalline Silicon for Enhancing Optical Properties,” Journal of Materials Processing Technology, Vol. 201, No. 1-3, pp. 291-296 (2008).
14. Tunna, L., Kearns, A., and Sutcliffe, C.J., “Micromachining of Copper using Nd:YAG Laser Radiation at 1064, 532, and 355 nm Wavelengths,” Optics & Laser Technology, Vol. 33, No. 3, pp. 135-143 (2001).
15. Dowling, A.J., Ghantasala, M.K., Hayes, J.P., Harvey, J.P., and Doyle, E.D., “Excimer Laser Micromachining of TiN Films from Chromium and Copper Sacrificial Layers,” Smart Materials and Structures, Vol. 11, No. 715-721 (2002).
16. Altan, M., “Reducing Shrinkage in Injection Moldings via the Taguchi, ANOVA and Neural Network Methods,” Materials & Design, Vol. 31, No.1, pp. 599-604 (2010).
17. Jean, M.D., Liu, C.D., and Wang, J.T., “Design and Development of Artificial Neural Networks for Depositing Powders in Coating Treatment,” Applied Surface Science, Vol. 245, No. 1-4, pp. 290-303 (2005).
18. Kuo, C.F.J., Tsai, W.L., Su, T.L., and Chen, J.L., “Application of an LM-neural Network for Establishing a Prediction System of Quality Characteristics for the LGP Manufactured by CO2 Laser,” Optics & Laser Technology, Vol. 43, No.3, pp. 529-536 (2011).
19. Liao, H.C., and Chang, H.H., “The Optimal Approach for Parameter Settings Based on Adjustable Contracting Capacity for the Hospital Supply Chain Logistics System,” Expert Systems With Applications, Vol. 38, No. 5, pp. 4790-4797 (2011).
20. Kamal, M.R., Patterson, W.I., Conley, N., Fara, D.A., and Lohfink, G., “Dynamics and Control of Pressure in the Injection Molding of Thermoplastics,” Polymer Engineering & Science, Vol. 27, No. 18, pp. 1403-1410 (1987).
21. Gao, F., Patterson, W.I., and Kamal, M.R., “Self-tuning Cavity Pressure Control of Injection Molding Filling,” Advances in Polymer Technology, Vol. 13, No. 2, pp. 111-120 (1994).
22. Gao, F., Patterson, W.I., and Kamal, M.R., Cavity Pressure Dynamics and Self-tuning Control for Filling and Packing Phases of Thermoplastics Injection Molding,” Polymer Engineering & Science, Vol. 36, No. 9, pp. 1272-85 (1996).
23. Yang, Y., and Gao, F., “Cycle-to-cycle and Within-cycle Adaptive Control of Nozzle Pressure during Packing-Holding for Thermoplastics Injection Molding,“ Polymer Engineering & Science, Vol. 39, No. 10, pp. 2042-2063 (1999).
24. Kazmer, D., and Barkan, P., “Multi-cavity Pressure Control in the Filling and Packing Stages of the Injection Molding Process,” Polymer Engineering & Science, Vol. 37, No. 11, pp. 1865-1879 (1997).
25. Varela, A.E., Self-tuning Pressure Control in an Injection Moulding Cavity during Filling. Chemical Engineering Research and Design, Vol. 78(A), pp. 79-86 (2000).
26. Potsch G, Michaeli W. Injection Molding: An Introduction, Hanser Publisher, Munich (1995).
27. Ross, P.J., Taguchi Techniques for Quality Engineering, 2nd ed, McGraw-Hill Inc., (1996).
28. Cooper, W.W., Seiford, L.M., and Tone, K., Introduction to Data Envelopment Analysis and Its Uses, Springer Science and Business Media, NewYork (2006).
29. Charnes, A., Cooper, W.W., and Rhodes, E., “Measuring the Efficiency of Decision Making Units,” European Journal of Operational Research, Vol. 2, No. 6, pp. 429-44 (1978).
30. Refaie, A.A, Wu T.H., and Li M.H., “Data Envelopment Analysis Approaches for Solving the Multiresponse Problem in Taguchi method,” Artificial Intelligence for Engineering Design, Analysis and Manufacturing, Vol. 23, pp. 159-173 (2009).
31. Astrom, K.J., Wittenmark, B., Adaptive control, 2nd ed, Pearson Addison Wesley (2006).
32. Hsu, L., Lizarralde, F., Araujo, A.D., “New Results on Output-Feedback Variable Structures Model-Reference Adaptive Control: Design and Stability Analysis,” IEEE Transactions on Automatic Control, Vol. 42, No. 3, pp. 386-93 (1997).
33. Sastry, S.S, and Bodson, M., Adaptive Control: Stability, Convergence and Robustness, Prentice-Hall (1989).
34. Liang, S.W., “Optimization of Microcrystalline Silicon Thin Film Solar Cell Isolation Processing Parameters by using Laser Technology,” Master Thesis, National Taiwan University of Science and Technology (2009).
35. Nise, N.S., Control System Engineering, 4th ed, Wiley International Edition (2004).

全文公開日期 2013/08/01 (校內網路)
全文公開日期本全文未授權公開 (校外網路)
全文公開日期 2013/08/01 (國家圖書館：臺灣博碩士論文系統)

簡易檢索 / 詳目顯示

相關論文