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研究生:梁瑞閔
研究生(外文):Jui-Ming Liang
論文名稱:智慧型程序控制整合於射出成形之分析
論文名稱(外文):ANALYSIS OF INTEGRATED INTELLIGENT PROCESS CONTROL IN INJECTION MOLDING
指導教授:王培仁
指導教授(外文):Pei-Jen Wang
學位類別:博士
校院名稱:國立清華大學
系所名稱:動力機械工程學系
學門:工程學門
學類:機械工程學類
論文種類:學術論文
論文出版年:2002
畢業學年度:90
語文別:英文
論文頁數:146
中文關鍵詞:射出成形輻射基類神經網路最佳化控制
外文關鍵詞:Injection MoldingRadial Based Function NetworksOptimization
相關次數:
  • 被引用被引用:8
  • 點閱點閱:194
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  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:1
隨著快速進步之電腦科技與逐漸成熟的電腦輔助工程技術,射出成形已儼然成為精密產品之主要生產途徑,如何改善產品生產效率及提高產品品質,勢必成為當前之首要研究課題。本論文先整理與分析過去數十年來有關射出成形之程序控制文獻與相關理論,再詳盡研究探討可改善產品品質之途徑與驗證方式,並考慮品質工程上所可能採用之方案,進行各種可行性探討與實驗設計分析,再參考類神經網路建模理論與步驟,嘗試改良程序控制器基本架構。故針對射出成形之程序控制,提出逆向類神經最佳化控制結構,串聯兩組輻射基類神經網路,令其一為控制器,另一為品質預測器,同時採用多重損失函數為一效能指標,藉以進行最佳化控制。此結構所構建射出成形參數與品質特性方式具下列之優點:一為可適當地的控制多種品質特性,另一為可調整權重以適應不同的品質組合要求。經由數值模擬及實驗的驗證,確可使射出成形過程完全自動化,並使操作參數逼近於最佳射出之參數條件;即便當次射出無法使成形品具最小的總品質損失,控制器仍將調整射出機之成形參數以減少總品質損失,此法不僅可應用於尋找射出機操作參數的起始設定,更可於生產時監看射出機操作參數之狀態,再於以調整至最佳操作點。經驗證實驗結果顯示,本文所提出之逆向類神經最化控制系統,可以依產品品質需求,在少數的幾模週期內,自動地穩定於近似最佳點上持續操作。
With the vast development on computer hardware as well as computer aided engineering (CAE) software, injection molding has become one of the primary methods for producing precision industrial products. Hence, the problems on improving both production efficiency and product quality need to be immediately studied and solved today. After reviewing the literatures and theoretical background concerning the process control for injection molding in the past decades, the study on quality improvement and experimental verification has been thoroughly conducted in this dissertation. With the considerations for quality engineering methodology, various viability studies and design of experiments have been accomplished. Also, improvements on the basic process control technology have been tested based upon the theoretical approach via neural networks modeling. In this study, a novel optimization scheme, namely inverse neural optimal control system (INOCS), is proposed for the process control with two serially connected radial basis functions networks (RBFN) that act as a quality predictor and an optimal controller relying on a multi-losses function based performance index. The proposed INOCS controller can appropriately handle multi-qualities or various combinations of qualities with prescribed weightings. After being verified by numerical simulations and experiments, the injection molding process could be fully automated with processing parameters set to the optimal conditions via the minimization of the total loss index. The INOCS controller works for not only the initialization of the parameters during startup but the optimization and adaptation of parameters during the process. In conclusion, the controller should meet the quality requirements and maintain steady operating conditions in less than a few cycles.
摘 要
ABSTRACT
ACKNOWLEDGEMENT
TABLE OF CONTENTS I
LIST OF TABLES AND FIGURES III
NOMENCLATURES AND NOTATIONS IX
CHAPTER 1 INTRODUCTION 1
1.1 Objective of the Study 2
1.2 Literatures Review 4
1.3 Scope of Work 18
CHAPTER 2 PREDICTIVE AND CONTROL MODEL 23
2.1 Problem Descriptions 23
2.2 Fundamental Assumptions 34
2.3 Design of Experiments 36
2.4 Radial Basis Function Networks 36
2.5 Inverse Neural Networks 38
CHAPTER 3 QUALITY CHARACTERISTICS AND PREDICTIONS42
3.1 Qualities and Losses 42
3.2 System Inputs and Outputs 51
3.3 Process Models 54
3.4 CAE Simulations 55
3.5 Screening Procedures 57
3.6 Convergence Criterion 58
CHAPTER 4 PROCESS CONTROL AND SIMULATIONS 61
4.1 Controller Structures 61
4.2 Characteristics of INOCS 67
4.3 Numerical Simulations 68
4.4 Concluding Remarks and Summary 74
CHAPTER 5 MODEL-BASED PROCESS CONTROL 101
5.1 Process Parameters Screening 102
5.2 On-Line Control Experiment 102
5.3 Concluding Remarks 112
CHAPTER 6 CONCLUSIONS AND FUTURE WORK 135
6.1 Conclusions 137
6.2 Future Works 140
BIBLIOGRAPHY 142
Ames, A. E., Mattucci, N., Macdonald, S., Szonyi, G., and Hawkins, D. M., 1997, “Quality Loss Functions for Optimization Across Multiple Response Surface,” Journal of Quality Technology, Vol. 29, No. 3, July, pp. 339-346.
Agrawal, A. R., Pandelidis, I. O., and Pecht, M., 1987, “Injection-Molding Process Control — A Review,” Polymer Engineering and Science, Vol. 27, No. 18, pp. 1345-1357.
Azouzi, R., and Guillot, M., 1998, “On-Line Optimization of Turning Process Using an Inverse Process Neurocontroller,” Journal of Manufacturing Science and Engineering, Vol. 120, pp. 101-108.
Blyskal, P. J., and Meheran, P. J., 1994, “Applying Design of Experiment Analysis Techniques to the Injection Molding Process,” Society of Plastic Engineers, ANTEC paper, pp. 729-732.
Chen, R. S., Lee, H. H., and Yu, C. Y., 1997, “Application of Taguchi’s Method on the Optimal Process Design of an Injection Molded PC/PBT Automobile Bumper,” Composite Structures, Vol. 39, pp. 209-214.
Cheng, B. W., Maghsoodloo, S., 1995, “Optimization of Mechanical Assembly Tolerances by Incorporating Taguchi’s Quality Loss Function,” Journal of Manufacturing System, Vol. 14, No. 4, pp. 264-276.
Choi, G. H., Lee, K. D., Chang, N., and Kim, S. G., 1994, “Optimization of Process Parameters of Injection Molding with Neural Network Application in a Process Simulation Environment,” CIRP Annuals, Vol. 43, No. 1, pp. 449-452.
Cox, H. W., and Mentzer, C., 1986, “Injection Molding: The Effect of Fill Time on Properties,” Polymer Engineering and Science, Vol. 26, No. 7, pp. 488-498.
Demirci, H. H., and Coulter, J. P., 1996, “A Comparative Study of Nonlinear Optimization and Taguchi Methods Applied to the Intelligent Control of Manufacturing Processes,” Journal of Intelligent Manufacturing, Vol. 7, pp. 23-38.
Dirion, J. L., Cabassud, M., Lelann, M. V., and Casamatta, G., 1995, “Design of a Neural Controller by Inverse Modeling,” Computers and Chemical Engineering, Vol. 19, pp. s797-s802.
Farrell, R. E., and Dzeskiewicz, L., 1994, “Expert System for Injection Molding,” Society of Plastic Engineers, ANTEC paper, pp. 692-695.
Gao, D. M., Nguyen, K. T., Girard, P., and Salloum, G., 1994, “Effect of Variable Injection Speed in Injection Mould Filling,” Society of Plastic Engineers, ANTEC paper, pp. 712-715.
Gao, F., and Yang, Y., 1997, “Multi-Variable Interaction Analysis and Proposed Quality Control System for Thermoplastics Injection Molding,” Society of Plastic Engineers, ANTEC paper, pp. 480-486.
Gillmann, D., and Neff, P., “Autoflow Automatic Injection Molding Control,” TOC of EUFIT ’98 Proceedings, Vol. 3, pp. 1486-1491.
Haeussler, J., and Wortberg J., 1993, “Quality Assurance in Injection Molding with Neural Networks,” Society of Plastic Engineers, ANTEC paper, pp. 123-129.
Haeussler, J., and Wortberg, J., 1996, “Quality Control in Injection Molding with an Adaptive Process Model Based on Neural Networks,” Society of Plastic Engineers, ANTEC paper, pp. 537-541.
Hunkar, D. B., 1987, “On-Line Statistical Process Control in Injection Molding,” Society of Plastic Engineers, ANTEC paper, pp. 275-279.
Ivester, R., and Danai, K., 1998, “ Automatic Tuning and Regulation of Injection Molding by the Virtual Search Method,” Journal of Manufacturing Science and Engineering, Vol. 120, pp. 323-329.
Jan, T. C., and O’Brien, K. T., 1993, “A User-Friendly, Interactive Expert System for the Injection Moulding of Engineering Thermoplastics,” The International Journal of Advanced Manufacture Technology, No. 8, pp. 42-51.
Jang, J. S., 1995, “Neural-Fuzzy Molding and Control,” Proceeding of IEEE, Vol. 83, No. 3, pp. 378-404.
Jansen, K. M. B., and Titomanlio, G., 1996, “Effect of Pressure History on Shrinkage and Residual Stress — Injection Molding with Constrained Shrinkage,” Polymer Engineering and Science, Vol. 36, No. 15, pp. 2029-2040.
Juvva, K. D., Mallik, R. K., and Murty, Y. S.N., 1995, “An Intelligent Controller Using a Self-learning Neural Net,” IEEE/IAS International Conference on Industrial Automation and Control, IA&C, pp. 235-238.
Kameoka, S., Haramoto, N., and Sakai, T., 1993, “Development of an Expert System for Injection Molding Operations,” Advance in Polymer Technology, Vol. 12, No. 4, pp. 403-418.
Kazmer, D., Roland, J., and Sherbelis G., 1997, “The Foundations of Intelligent Process Control for Injection Molding,” Journal of Injection Molding Technology, Vol. 1, No. 1, pp. 44-56.
Kazmer, D. O., and Petrova, T., 1997, “Synthesis and Analysis of Quality Control Methods for Intelligent Processing of Polymeric Materials,” CAE and Intelligent Processing of Polymeric Materials ASME, Vol. 79, p.249-264.
Kim, B. H., Hwang, T. W., and Park, H. J., 1995, “Predicting Mechanical Properties of Molded Plaque and Box,” Polymer Engineering and Science, Vol. 25, No. 15, pp. 1252-1259.
Kohlhepp, K., 1986, “Acetalcopolymer: Injection Molding of Precision Injection Parts/Quality Control by Part Weight Measurement,” Society of Plastic Engineers, ANTEC Paper, pp. 124-127.
Kyle, B. R. M., 1990, “Experimental Design and Injection Moulding,” Society of Plastic Engineers, ANTEC Paper, pp. 343-347.
Leo, V., and Cuvelliez, C., 1996, “The Effect of the Packing Parameters, Gate Geometry, and Mold Elasticity on the Final Dimensions of a Molded Part,” Polymer Engineering and Science, Vol. 36, No. 15, pp. 1961-1972.
Liu, C., and Manzione, L. T., 1996, “Process Studies in Precision Injection Molding. I: Process Parameters and Precision,” Polymer Engineering and Science, Vol. 36, No. 1, pp. 1-10.
Mapleston, P., 1999, “Real-Time Process Control Is Said to Provide Perfect Shots,” Modern Plastics, Vol. 29, pp. 29-30.
Michaeli, W., and Vaculik, R., 1995, “Closed Loop Quality Control for Injection Molding Based on Statistical Process Models,” Society of Plastic Engineers, ANTEC paper, pp. 4046-4050.
Mok, S. L., Kwong, C. K., and Lau, W. S., 1999, “Review of Research in the Determination of Process Parameters for Plastic Injection Molding,” Advances in Polymer Technology, Vol. 18, No. 3, pp. 225-236.
Oakland, J. S., 1996, Statistical Process Control, Third Edition, Butterworth-Heinemann, Oxford.
Orezchowski S., Paris A., Dobibin C. J. B., 1998, “A Process Monitoring and Control System for Injection Molding Using Nozzle-Based Pressure and Temperature Sensors,” Society of Plastic Engineers, ANTEC paper, pp. 424-430.
Pandelidis, I. O., and Agrawal, A. R., 1988, “Optimal Anticipatory Control of Ram Velocity in Injection Molding,” Polymer Engineering and Science, Vol. 28, No. 3, pp. 147-155.
Phadke, M. S., 1989, Quality Engineering Using Robust Design, P T R Prentice-Hall, Inc., N.J.
Potente, H., Wortberg, J., Hanning, D., and Haubler, J., 1993, “Process Monitoring in Plastics Processing a Number of Critical Observations,” Society of Plastic Engineers, ANTEC paper, pp. 579-584.
Potente, H., Du, Y. H., Wenniges, T., and Neumann, H., 1995, “Quantitative Estimation of Fuzzy Classifier Applying To The Quality Control in Plastics Processing,” Society of Plastic Engineers, ANTEC paper, pp. 3996-4001.
Rosato, Dominick V., and Rosato, Donald V., 1986, Injection Molding Handbook — The Compiete Molding Operation Technology, Performance, Economic, Van Nostrand Reinhold, New York.
Rowland, J. C., and Kazmer, D. O., 1996, “An Online Quality Monitoring System for Thermoplastic Injection Molding,” Society of Plastic Engineers, ANTEC paper, pp. 513-518.
Rowland, J. C., and Kazmer, D. O., 1997, “Quantifying the Economic Value Added of On-Line Quality Control Systems,” Society of Plastic Engineers, ANTEC paper, pp. 399-404.
Sanschagrin, B., 1983, “Process Control of Molding,” Polymer Engineering and Science, Vol. 23, No. 8, pp. 431-438.
Schnerr, O., and Michaeli, W., 1998, “Neural Networks for Quality Prediction and Closed-Loop Quality Control in Automotive Industry,” Society of Plastic Engineers, ANTEC paper, pp. 660-664.
Scott, R. C., and Pado, L. E., 1998, “Active Control of Wind-Tunnel Model Aeroelastic Response Using Neural Networks,” Journal of Guidance, Control, and Dynamics, Vol. 23, No. 6, pp. 1100-1109.
Seaman, C. M., Desrochers, A. A., and List, G. F., 1993, “A Multiobjective Optimization Approach to Quality Control with Application to Plastic Injection Molding,” IEEE Transactions on Systems, Man and Cybernetics, Vol. 23, No. 2, pp. 414-425.
ercer, M., atic, I., Baric, G., and Perkovic, D., 1997, “Trend Regulation of Injection Moulding Process,” Society of Plastics Engineers, ANTEC paper, pp. 556-560.
Sherbelis, G., Garvey, E., and Kazmer, D., 1997, “The Methods and Benefits of Establishing a Process Window,” Society of Plastics Engineers, ANTEC paper, pp. 545-550.
Shing, O. N., 1999, “Design for Manufacture of a Cost-Based System for Molded Parts,” Advances in Polymer Technology, Vol. 18, No. 1, pp. 33-42.
Souder, B., and Woll S., 1994, “Advanced Method for Monitoring Injection Molding Process,” Society of Plastic Engineers, ANTEC paper, pp. 644-650.
Speight, R. G., Yazbak, E. P., and Coats, P. D., 1996, “In Line Process Control Measurement for Integrated Injection Molding,” Society of Plastic Engineers, ANTEC paper, pp. 508-512.
Taghizadegan, S., 1996, “Statistical Process Control of Injection Molding Simulation Based on an Experimental Study,” Society of Plastic Engineers, ANTEC paper, pp. 598-602.
Thibault, J., and Grandjean, B. P. A., 1991, “Neural Networks in Process Control — A Survey,” IFAC International Symposium Advanced Control of Chemical Process, ADCHEM, pp. 295-304.
Wang, K. K., and Zhou, J., 1999, “An Integrated Adaptive Control for Injection Molding,” Society of Plastic Engineers, ANTEC paper, pp. 611-615.
Wang, P. J., Liang, J. M., 1999, “A Numerical Virtual Process Modeler Based on Computer Aided Engineering Software for Injection Molding,” Society of Plastic Engineers, ANTEC paper, pp. 680-684.
Wang, P. J., Lin, J. Y., 1997, “A Novel Process Control for Injection Molding Based Upon On-Line CAE Systems,” CAE and Intelligent Processing of Polymeric Materials ASME, MD-Vol. 79, pp. 265-270.
Woll, S. L. B., Cooper, D. J., and Souder, Blair V., 1996, “Online Pattern-Based Part Quality Monitoring of the Injection Molding Process,” Polymer Engineering and Science, Vol. 36, No. 11, pp. 1477-1488.
Wortberg, J., Walter, A., and Mustafa, M. A., 1997, “Process-Related Quality Management,” Kunststoffe Plast Europe, Vol. 11, pp. 1664-1668.
Wu, C. C., Chen, Z., and Tang, G. R., 1998, “Component Tolerance Design for Minimum Quality Loss and Manufacturing Cost,” Computers in Industry, Vol. 35, pp. 223-232.
Wu, J. L., Chen, S. J., and Malloy, R., 1991, “Development of an On-Line Cavity Pressure-Based Expert System for Injection Molding Process,” Society of Plastic Engineers, ANTEC paper, pp. 444-468.
Ye, H., Wu, Y., and Wang, K. K., 1997, “An Optimization Scheme for Part Quality in Injection Molding,” CAE and Intelligent Processing of Polymeric Materials, ASME, MD-Vol. 79, pp. 139-149.
Yeung, V. W. S., and Lau, K. H., 1997, “Injection Moulding, ‘C-MOLD’ CAE Package, Process Parameter Design and Quality Function Deployment: A Case Study of Intelligent materials Processing,” Journal of materials Process Technology, Vol. 63, pp. 481-487.
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