第五章 製程控制
6.2 未來研究方向
在未來研究方向部份，可以結合多目標品質上，進行多因子多品質目標 之控制研究，因此將未來之研究方向條列如下：
一、由於本研究之架構為多因子單品質目標(MISO)，因此未來將嚐試以 類神經網路結合修正式牛頓法與 DFP 法來對射出成型製程參數進 行多因子多品質目標(MIMO)控制法進行研究。
二、在微射出的應用上，其預測誤差的需求要在 1x10-4以下，因此若想 將此方法應用於微射出上，還需要增加預測精度，未來將結合其它 預測方式達成此精度。
三、以 AI 類神經網路應用於射出成型之品質變異與生產環境之相關性 研究。在精密射出之生產過程中，由於生產環境之溫度、濕度與大 氣壓力之改變，會造成熱平衡之改變，因此會造成產品品質之變異，
其相關性為何? 目前只知道有相關，至於相關強度為何，目前還未 知。
四、由於在射出成型之品質判斷上，有些是主觀條件之判斷，如:外觀、
觸感…，若這些主觀條件能夠透過機械視覺辨識系統與以量化，會 有助於製程控制之精確度
五、以本研究之控制架構應用於其他控制領域，嚐試將模糊類神經 (Fuzzy Neural Network)應用於工業動態控制系統上。
上述幾項為將來所要研究之主題，當然在上述主題所衍生出來的研究將 會非常多，相對的挑戰也會隨之而來。期望有機會能夠完成上述之各項研究。
參考文獻
1. 千阪淺之助 (1995),「射出成型技術入門」,建宏出版社 2. 田口玄ㄧ,”Introduction to Design of Experiment”,滄海書局
3. 吳政鴻 (2003),「自調整模糊控制於射出成型機速度控制之研究」,成功大 學造船暨船舶機械工程研究所碩士論文
4. 辛宜芳 (2003),「以 CAD 為平台之自動排版系統使用基因演算法」,中華 大學 科技管理研究所論文
5. 林立偉 (2001),「應用多變量製程能力指標於多品質特性之新速製程」,交 通大學 工業工程管理研究所論文
6. 范樂陽,「塑膠加工實務」,高立圖書有限公司。
7. 孫孝慶 (2000),「以 CAD 為基礎之鈑金件自動化展開系統使用類神經網路 計算折彎延伸量」,中華大學 工業管理研究所論文
8. 郭世洲 (2001),「使用多目標基因演算法於傳導性電磁干擾濾波器之最佳 化參數推導」,中華大學 工業管理研究所論文
9. 莊信源 (2000),「類神經模糊系統與遺傳演算法在加工參數最佳化之應用」, 台灣海洋大學 機輪所碩士論文
10. 黃臣鴻 (2003),「PC/ABS 合膠機械性質之射出成型條件最佳化」,中央大 學 機械工程研究所博士論文
11. 陳啟峰 (2001),「塑膠光學透鏡之射出成型製程探討」,長庚大學 機械工 程研究所碩士論文
12. 葉怡成 著 (1998),「類神經網路模式應用與實作」,儒林圖書有限公司 13. 許壽文 (2003),「類神經網路應用於發光二極體檢測」,中華大學 科技管
理研究所
14. 梁瑞閔 (2002),「智慧型程序控制整合於射出成型之分析」,清華大學 動 力機械工程學系博士論文
15. 曾國勳 (2002),「資料挖掘在射出成型之研究」,淡江大學 資訊工程研究 所碩士論文
16. 湯燦泰 (2001),「熔融紡絲系統參數最佳化之研究」,台灣科技大學 纖維 及高分子工程系碩士論文
17. 楊景程 (2001),「射出成型機最佳參數之預測」,台灣科技大學 纖維及高 分子工程系碩士論文
18. 張斐章、張麗秋、黃浩倫 著(2004),「類神經網路 理論與實務」,東華書 局
19. 張榮語 著(1995),「射出成形模具設計:模具設計」,高立圖書有限公司 20. 蔡子琦 (1998),「類神經網路與基因遺傳演算法於 WEDM 加工參數最佳
化之應用」,臺灣大學 機械工程學研究所碩士論文
21. 蘇木春、張孝德 著(1999),「機器學習:類神經網路、模糊系統以及基因演 算法」,全華書局
22. Alam, K. and Kamal, M.R.(2004), “Runner Balancing by a Direct Genetic Optimization of Shrinkage”, Polymer Engineering and Science,Vol. 44, No.10,pp. 1949-1959
23. Anderson, D.A., Tannehill, J.C. and Pletcher, R.H. (1990),Computational Fluid Mechanics and Heat Transfer, McGraw-Hill
24. Arabshahi, P., Choi, J.J., Marks, R.J., and Caudell, T. P. (1996), “Fuzzy Parameter Adaptation in Optimization : Some Neural net Training Examples”, IEEE Computational Science & Engineering, Vol. 3,No. 1,pp.57-65.
25. Arora, J. S. (1989),Introduction to OPTIMUM DESIGN,McGraw-Hill
26. Atkinson, K.E.(1986),An Introduction to NUMERICAL ANALYSIS,Secand Edition, John Wiley and Sons (WIE)
27. Bhatikar, S.R. and Mahajan, R.L. (2002),”Artificial Neural-Network-Based Diagnosis of CVD Barrel Reactor”, IEEE Transactions on semiconductor vol.15 No.1.
28. Chen, W.C., Lee, H.I. , Deng, W.J. and Liu, K.Y. (2006),“The implementation of neural network for semiconductor PECVD process”, Expert Systems with Applications, Article in Press.
29. Chen, W.C. ,Chen, T.C. ,Tsai, C.H. and Ho, T.H. (2006),“The Neural Network Implementation in Pattern Recognition of Semiconductor Etching Process”, Special Issue on Quality and Reliability Management of JCIIE.
30. Chiu, C.P., Shin, M.C. and Wei, J.H. (1991), “Dynamic Modeling of the Filling Process in an Injection Molding Machine”, Polymer Engineering and Science, Vol. 31, No. 19, pp.1417-1425
31. 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
449-452.
32. Chris, M.S. and George, F. (1994),”Multiobjective Optimization of a Plastic Injection Molding Process”, IEEE Transactions on Control Systems Technology, Vol. 2, No. 3, pp. 157-168.
33. Cook, D.F., Ragsdale, C.T. and Major, R.L.(2000)，”Combining a neural network with a genetic algorithm for process parameter optimization”,Engineering Applications of Artificial Intelligence Vol.13,Iss.4 34. Costa, N. and Ribeiro, B. (1999),”Artificial Neural Network For Data
Modeling of Plastic Injection Process”,IEEE 1999
35. Covas. J.A., Cunha, A.G. and Oliveira, P. (1999),“An optimization Approach to Practical Problem in Plasticating Single Screw Extrusion”,Polymer Engineering and Science,Vol 39.
36. Drummer, H. (2004),” New role of R2R control for CMP processes at new selfalligned contact integration scheme", 5th European Advanced Equipment Control/Advanced Process Control (AEC/APC) Conference.
37. Fara, D.A., Kamal, M.R. and Patterson, W. I. (1985), “Evaluayion of Simple Dynamic Models and Controllers for Hydyaulic and Nozzle Pressure in Injection Molding”, Polymer Engineering and Science, Vol. 25, No. 11, pp.
714-723.
38. Fara, D. A., Rawabdeh, I. and Dia, A.A.N. (2001),” Neural network control for cavity pressure during filling and packing stages of the thermoplastics injection molding process” , Journal of Injection Molding Technology.
Vol.5, Iss. 2, pp. 105-114.
39. Fletcher, R. and Powell, M.J.D. (1963),”A Rapidly Convergent Descent Method for Minimization”, The Computer Journal ,Vol. 6,pp. 163-180
40. Gao, F. (2003),“Injection Molding Packing Profile - Toward Closed-Loop Quality Control”， 國際化短期密集訓練-先進材料，成型與模具技術,中原 大學.
41. Gen, M., and Cheng, R. (1997), Genetic Algorithms and Engineering Design,Wiley.
42. Goldberg, D.E. (1989), Genetic Algorithms in search optimization and machine learning, Addison – WESLEY publishing Company.
43. Gower-Hall, A.E. (2001),”Integrated Model-Based Run-to-Run Uniformity
Control for Epitaxial Silicon Deposition",Microsystems Technology Laboratories Massachusetts Institute of Technology.
44. Gurer, E., Zhong, T., Lewellen, J. and Reynolds, R. (1998),”Model-based Adaptive Process Control: APCVD-control Example", Solid State Technology 45. Han , S.S. and May, G.S. (1997),“Using Neural Network Process Models to
Perform PECVD Silicon Dioxide Recipe Synthesis via Genetic Algorithms”, IEEE Transactions on Semiconductor Manufacturing,Vol. 10, No. 2,pp.
279-287
46. He, W. , Zhang, Y.F., Lee, K.S. , Fuh J.Y.H., and Nee, A.Y.C. (1998),
“Automated Process Parameter Resetting for Injection Molding : a Fuzzy-neuro Approach”,Journal of Intelligent Manufacturing, Vol. 9, No. 1, pp. 17-27.
47. Helps, C. ,Richard, G., Strong, A.B.,Raed A.Z.,Hawks, V.D. and Kohkonen, K.E. (1999),“The use of artificial neural networks in the prediction of machine operational Setting for injection molded parts”, Journal of Injection Molding Technology; Career and Technical Education, pp. 201-211
48. Holland, J. H. (1975), “Adaptation in Nature and Artificial Systems”, MIT Press.
49. Hurwitz, A. (1996),”Advanced Process Control: Run-to-run Process Control Development – Interim Report", SEMATECH.
50. Jacobs, R. A.(1988), “Increased Rate of Convergence through Learning Rate Adaptation”, Neural Network, Vol. 1, pp.295-307
51. Junghui and Liao, C.M. (2002),“Dynamic process fault monitoring based on neural network and PCA”, Journal of Process Control, No.12.
52. Karmal, M.R., Varela, A.E. and Patterson, W.I. (1996), “Control of Part Weight in Injection Molding of Amorphous Thermoplastics”,Polymer Engineering and Science,Vol. 39,No.5,pp.940-952.
53. Kecman, V. (2001),”Learning and Soft Computing:Support Vector Machines,Neural Networks,and Fuzzy Logic Models”,Combridge,MA:MIT press.
54. Khaw , F.C., Lim, B.S. and Lim, E.N.(1995), 〝Optimal design of neural network using the Taguchi method〞, Neurocomputing ,Vol. 7, Iss. 3, pp.
55. Kim, B. and May, G.S. (1995),”Real-Time Diagnosis of Semiconductor Manufacturing Equipment”, Electronics Manufacturing Technology Symposium, pp.224-231
56. Kuang, W., Ni, K. and Zhang, S. (2002),” Optimization method for fitting three parameters of P-S-N curve under constant amplitude loading.”, Ocean Engineering/Haiyang Gongcheng. Vol. 20, no. 2, pp. 74-77.
57. Lau, H.C.W. , Ning, A., Pun, K.F. and Chin, K.S. (2001),” Neural networks for the dimensional control of molded parts based on a reverse process model” ,Journal of Materials Processing Technology Vol.117,pp.89~96.
58. Li, E., Jia, L. and Yu, J. (2002), “A genetic neural fuzzy system-based quality prediction model for injection process”, Computers and Chemical Engineering 26 pp.1253–1263.
59. Liang, J.M. and Wang, P.J. (2002),”Multi-objective optimization scheme for quality control in injection molding”, Journal of Injection Molding Technology, Career and Technical Education , pp. 331-342
60. Liang, J.M. and Wang P.J. (2002),” Self-learning control for injection molding based on neural networks optimization”,Journal of Injection Molding Technology. Vol.6, Iss. 1, pp. 58- 72.
61. Liou, S.R. and Yang,C.D. (2000), “Application of Genetic Algorithms and Taguchi Methods to Flight Control Design”, Transactions of the Aeronautical and Astronautical Society of the Republic of China, Vol. 32, No. 3, pp.
223-236.
62. Lotti, C. , Ueki, M.M. and Bretas, R.E.S. ( 2002),” Prediction of the shrinkage of injection molded iPP plaques using artificial neural networks”, Journal of Injection Molding Technology. Vol.6, Iss. 3; pp.157-176 .
63. Luenberger, D.G. (1984),Linear and Nonlinear Programming, Addison -Wesley ,Reading,M.A.
64. Mathur, R., Advani, S.G. and Fink, B.K. (1998),“Optimization of Gate and Vent Locations for Resin Infusion Processes Using Genetic Algorithms”, Proceedings of the American Control Conference Philadelphia, Pennsylvania, pp. 2176-2180
65. Min, B.H. and Shin, B.C. (2001) ,” A study on volumetric shrinkage of injection molded parts based on neural networks”, Journal of Injection
Molding Technology. Vol.5 , Iss. 4, pp.201-207
66. Moyne, J.R., Telfeyan, R., Hurwitz, A., and Taylor, J. (1995),”A Process-independent Run-to-run Controller and Its Application to Chemical- mechanical Planarization", IEEE/SEMI Advanced Semiconductor Manfacturing Conference.
67. Mullins,T. (1997),Advanced Process Control Framework Initiaive (APCFI) 1.0 Specification, SEMATECH.
68. Petrova, T. and Kazmer, D. (1999), “Incorporation of Phenomenological Models in a Hybrid Neural for Quality Control of Injection Molding”,Polymer Plastic Technolgy Engineer, Vol. 38, No. 1, pp.1-18.
69. Sadeghi , B.H.M. (2000),“A BP-neural network predictor model for plastic injection molding process”, Journal of Materials Processing Technology ,Vol.103, pp.411~416.
70. Sanschagrin, B. (1983), “Process Control of Injection Molding,” Polymer Engineering and Science, Vol. 23, No. 8, pp. 431-438.
71. Shanker, A. and Paul, F. W. (1982), “A Mathematical Model for the Exaluation of Injection Molding Machine Control”, Journal of Dynamic Systems, Measurement and Control, Transactions of the ASME, Vol. 104, No.
1, pp. 86-92
72. Shen, C. , Yu, X., Wang, L. and Tian, Z. (2004),“Gate location optimization of plastic injection molding”, Journal of Chemical Industry and Engineering,Vol.
55,Iss. 3 ,pp. 445-449
73. Shi, F., Lou, Z.L., Lu, J.G. and Zhang, Y. Q. (2003),”Optimization of Plastic Injection Molding Process with Soft Computing”, International Journal Advanced Manufacturing Technology ,Vol. 21,pp. 656–661
74. Sridhar, D.V, Bartlett, E.B. and Seagrave, R.C. (1996),”An information theoretic approach for combining neural network process model”, Neural Network.
75. Suresh, P.V.S., Rao, P.V. and Deshmukh, S.G. (2002), “A genetic algorithmic approach for optimization of surface roughness prediction model ”International Journal of Machine Tools & Manufacture ,Vol. 42, Iss. 6, pp. 675-680
for NN Topology and Weights Optimization”, Proceedings of the 1st IEE/IEEE International Conference on Genetic Algorithms in Engineering Systems, pp. 250-255.
77. Tarng , Y.S., Yang , W.H., and Juang, S. C. (2000), “The Use of Fuzzy Logic in the Taguchi Method for the Optimisation of the Submerged Arc Welding Process”,International Journal of Advanced Manufacturing Technology, Vol.
16, pp. 688-694.
78. Wang, G.J., Tsai, J.C., Tseng, P.C., and Chen,T.C. (1998), “Neural-Taguchi Method for Robust Design Analysis”, Journal of the Chinese Society of Mechanical Engineers, Vol. 19, No. 2, pp. 223-230.
79. Wang, K.K. (1980), “System Approach to Injection Molding Process.”
Polymer-Plastics Technology and Engineering, Vol. 14, No. 1, pp. 75-93.
80. Yelverton, M., Tsakalis, K., and Stoddard, K. (1998),”Factory-wide Run-to-run Process Control”,Solid State Technology
81. Zhao, C. and Gao, F. (1999),”Melt Temperature Profile Prediction for Thermoplastic Injection Molding”, Polymer Engineering and Science, Vol. 39, No. 9, pp. 1787-1801.
82. Zurada, J.M. (1992),”Introduction to Artificial Neural System”,St.
Paul,MN:West Publishing
附錄 A
保壓時間與(射出時間與射出速度)之多元回歸分析資料 Run# Injection
Time Velocity
Packing
Time Run#Injection
Time Velocity
Packing Time 1 1.3 40 0.25 26 1.4 46 0.48 2 1.3 40 0.25 27 1.4 46 0.48 3 1.3 40 0.25 28 1.4 46 0.48 4 1.3 40 0.25 29 1.4 46 0.49 5 1.3 40 0.25 30 1.4 46 0.49 6 1.3 43 0.33 31 1.4 49 0.54 7 1.3 43 0.33 32 1.4 49 0.55 8 1.3 43 0.33 33 1.4 49 0.54 9 1.3 43 0.33 34 1.4 49 0.54 10 1.3 43 0.33 35 1.4 49 0.54 11 1.3 46 0.39 36 1.4 52 0.6 12 1.3 46 0.39 37 1.4 52 0.6 13 1.3 46 0.39 38 1.4 52 0.6 14 1.3 46 0.39 39 1.4 52 0.6 15 1.3 46 0.39 40 1.4 52 0.6 16 1.3 49 0.45 41 1.4 40 0.36 17 1.3 49 0.45 42 1.4 40 0.36 18 1.3 49 0.45 43 1.4 40 0.36 19 1.3 49 0.45 44 1.4 40 0.36 20 1.3 49 0.45 45 1.4 40 0.36 21 1.3 52 0.51 46 1.4 43 0.44 22 1.3 52 0.51 47 1.4 43 0.44 23 1.3 52 0.51 48 1.4 43 0.44 24 1.3 52 0.51 49 1.4 43 0.44 25 1.3 52 0.51 50 1.4 43 0.44
Run# Injection
Time Velocity
Packing
Time Run#Injection
Time Velocity
Packing Time
51 1.5 52 0.69 76 1.6 43 0.62 52 1.5 52 0.69 77 1.6 43 0.62 53 1.5 52 0.69 78 1.6 43 0.62 54 1.5 52 0.69 79 1.6 43 0.62 55 1.5 52 0.69 80 1.6 43 0.62 56 1.5 40 0.45 81 1.6 46 0.69 57 1.5 40 0.45 82 1.6 46 0.69 58 1.5 40 0.45 83 1.6 46 0.69 59 1.5 40 0.45 84 1.6 46 0.69 60 1.5 40 0.45 85 1.6 46 0.69 61 1.5 43 0.53 86 1.6 49 0.75 62 1.5 43 0.53 87 1.6 49 0.75 63 1.5 43 0.53 88 1.6 49 0.75 64 1.5 43 0.53 89 1.6 49 0.75 65 1.5 43 0.53 90 1.6 49 0.75
66 1.5 46 0.6 91 1.6 52 0.8
67 1.5 46 0.6 92 1.6 52 0.8
68 1.5 46 0.6 93 1.6 52 0.8
69 1.5 46 0.6 94 1.6 52 0.8
70 1.5 46 0.6 95 1.6 52 0.8
71 1.5 49 0.66 96 1.6 40 0.57 72 1.5 49 0.66 97 1.6 40 0.57 73 1.5 49 0.66 98 1.6 40 0.57 74 1.5 49 0.66 99 1.6 40 0.57 75 1.5 49 0.66 100 1.6 40 0.57
Run# Injection
Time Velocity
Packing
Time Run#Injection
Time Velocity
Packing Time
101 1.7 49 0.84 126 1.35 48.6 0.49 102 1.7 49 0.84 127 1.35 48.6 0.49 103 1.7 49 0.84 128 1.35 48.6 0.49 104 1.7 49 0.84 129 1.35 48.6 0.49 105 1.7 49 0.84 130 1.35 48.6 0.49 106 1.7 52 0.89 131 1.41 47.7 0.54 107 1.7 52 0.89 132 1.41 47.7 0.54 108 1.7 52 0.89 133 1.41 47.7 0.54 109 1.7 52 0.89 134 1.41 47.7 0.54 110 1.7 52 0.89 135 1.41 47.7 0.54 111 1.7 40 0.66 136 1.41 43.6 0.45 112 1.7 40 0.66 137 1.41 43.6 0.45 113 1.7 40 0.66 138 1.41 43.6 0.45 114 1.7 40 0.66 139 1.41 43.6 0.45 115 1.7 40 0.66 140 1.41 43.6 0.45 116 1.7 43 0.74 141 1.37 42 0.38 117 1.7 43 0.74 142 1.37 42 0.38 118 1.7 43 0.74 143 1.37 42 0.38 119 1.7 43 0.74 144 1.37 42 0.38 120 1.7 43 0.74 145 1.37 42 0.38 121 1.7 46 0.8 146 1.62 40.3 0.58 122 1.7 46 0.8 147 1.62 40.3 0.58 123 1.7 46 0.8 148 1.62 40.3 0.58 124 1.7 46 0.8 149 1.62 40.3 0.58 125 1.7 46 0.8 150 1.62 40.3 0.58
附錄 B
平均保壓壓力與(保壓壓力與保壓時間)之多元回歸分析資料
Run#
Input:
Packing Pressure
Packing Time
Avg.Pack pressure
Run#
Input:
Packing Pressure
Packing Time
Avg.Pack pressure 1 35 0.25 41.3444 26 40 0.48 40.2072 2 35 0.25 41.5231 27 40 0.48 40.3121 3 35 0.25 41.4103 28 40 0.48 40.1627 4 35 0.25 41.0614 29 40 0.49 40.3917 5 35 0.25 41.5359 30 40 0.49 40.4256 6 40 0.33 42.372 31 45 0.54 44.4013 7 40 0.33 42.4303 32 45 0.55 44.5399 8 40 0.33 42.4854 33 45 0.54 44.5109 9 40 0.33 42.624 34 45 0.54 44.2616 10 40 0.33 42.4901 35 45 0.54 44.4165 11 45 0.39 45.8814 36 50 0.6 48.9917 12 45 0.39 45.8404 37 50 0.6 48.8446 13 45 0.39 45.9401 38 50 0.6 48.8665 14 45 0.39 45.8053 39 50 0.6 48.7977 15 45 0.39 45.9207 40 50 0.6 48.9109 16 50 0.45 50.0507 41 55 0.36 55.3241 17 50 0.45 50.0346 42 55 0.36 55.2179 18 50 0.45 50.054 43 55 0.36 55.5051 19 50 0.45 49.8547 44 55 0.36 55.3722 20 50 0.45 50.1728 45 55 0.36 55.3767 21 55 0.51 54.4231 46 35 0.44 35.7229 22 55 0.51 54.2291 47 35 0.44 35.5044 23 55 0.51 54.115 48 35 0.44 35.4639 24 55 0.51 54.2236 49 35 0.44 35.9057 25 55 0.51 54.4238 50 35 0.44 35.7723
Run#
Input:
Packing Pressure
Packing Time
Avg.Pack pressure
Run#
Input:
Packing Pressure
Packing Time
Avg.Pack pressure 51 45 0.69 43.5531 76 50 0.62 48.5678 52 45 0.69 43.6576 77 50 0.62 48.5768 53 45 0.69 43.3827 78 50 0.62 48.6114 54 45 0.69 43.5057 79 50 0.62 48.5403 55 45 0.69 43.7166 80 50 0.62 48.4619 56 50 0.45 49.7937 81 55 0.69 53.1015 57 50 0.45 49.9634 82 55 0.69 53.1207 58 50 0.45 49.788 83 55 0.69 52.9782 59 50 0.45 49.8297 84 55 0.69 53.2037 60 50 0.45 49.7583 85 55 0.69 53.1486 61 55 0.53 53.9278 86 35 0.75 33.7198 62 55 0.53 54.0388 87 35 0.75 33.7811 63 55 0.53 53.9989 88 35 0.75 33.7053 64 55 0.53 54.0114 89 35 0.75 33.6465 65 55 0.53 53.8017 90 35 0.75 33.6192 66 35 0.6 34.4688 91 40 0.8 38.3229 67 35 0.6 34.3697 92 40 0.8 38.1928 68 35 0.6 34.2788 93 40 0.8 38.1353 69 35 0.6 34.3354 94 40 0.8 38.2683 70 35 0.6 34.5341 95 40 0.8 38.3176 71 40 0.66 38.8429 96 45 0.57 43.928 72 40 0.66 38.772 97 45 0.57 43.8874 73 40 0.66 38.8504 98 45 0.57 44.153 74 40 0.66 38.8681 99 45 0.57 43.9063 75 40 0.66 38.7939 100 45 0.57 43.9118
Run#
Input:
Packing Pressure
Packing Time
Avg.Pack pressure
Run#
Input:
Packing Pressure
Packing Time
Avg.Pack pressure 101 55 0.84 52.5358 126 40.3 0.49 40.6332 102 55 0.84 52.619 127 40.3 0.49 40.6783 103 55 0.84 52.6375 128 40.3 0.49 40.5516 104 55 0.84 52.6518 129 40.3 0.49 40.7806 105 55 0.84 52.6287 130 40.3 0.49 40.5748 106 35 0.89 33.1921 131 41.5 0.54 41.4207 107 35 0.89 33.1932 132 41.5 0.54 41.1186 108 35 0.89 33.2905 133 41.5 0.54 41.3964 109 35 0.89 33.2298 134 41.5 0.54 41.102 110 35 0.89 33.271 135 41.5 0.54 41.1707 111 40 0.66 38.6847 136 46.5 0.45 46.5732 112 40 0.66 38.7183 137 46.5 0.45 46.5968 113 40 0.66 38.734 138 46.5 0.45 46.7584 114 40 0.66 38.7164 139 46.5 0.45 46.5697 115 40 0.66 38.7074 140 46.5 0.45 46.7527 116 45 0.74 43.2098 141 46.4 0.38 47.4762 117 45 0.74 43.2437 142 46.4 0.38 47.3339 118 45 0.74 43.1538 143 46.4 0.38 47.2937 119 45 0.74 43.0615 144 46.4 0.38 47.4619 120 45 0.74 43.1046 145 46.4 0.38 47.4768 121 50 0.8 47.8252 146 39.9 0.58 38.9999 122 50 0.8 47.7129 147 39.9 0.58 39.1321 123 50 0.8 47.6991 148 39.9 0.58 39.2269 124 50 0.8 47.971 149 39.9 0.58 38.9522 125 50 0.8 47.7871 150 39.9 0.58 39.0587
附錄 C
螺桿最大行程與(保壓時間與平均保壓壓力)之多元回歸分析資料
Run#
Packing Time
Avg.Pack pressure
Ram Position at
Max. Filling Run#
Packing Time
Avg.Pack pressure
Ram Position at Max. Filling 1 0.25 41.3444 7.09 26 0.48 40.2072 6.93 2 0.25 41.5231 7.09 27 0.48 40.3121 6.82 3 0.25 41.4103 7.1 28 0.48 40.1627 6.95 4 0.25 41.0614 7.09 29 0.49 40.3917 7.06 5 0.25 41.5359 7.08 30 0.49 40.4256 6.88 6 0.33 42.372 6.96 31 0.54 44.4013 6.32 7 0.33 42.4303 6.93 32 0.55 44.5399 6.45 8 0.33 42.4854 6.94 33 0.54 44.5109 6.39 9 0.33 42.624 7.06 34 0.54 44.2616 6.04 10 0.33 42.4901 7.09 35 0.54 44.4165 6.61 11 0.39 45.8814 6.48 36 0.6 48.9917 5.85 12 0.39 45.8404 6.66 37 0.6 48.8446 6.1 13 0.39 45.9401 6.74 38 0.6 48.8665 6.03 14 0.39 45.8053 6.54 39 0.6 48.7977 5.98 15 0.39 45.9207 6.47 40 0.6 48.9109 5.97 16 0.45 50.0507 6.17 41 0.36 55.3241 5.82 17 0.45 50.0346 6.57 42 0.36 55.2179 5.63 18 0.45 50.054 6.12 43 0.36 55.5051 6.02 19 0.45 49.8547 6.2 44 0.36 55.3722 6.01 20 0.45 50.1728 6.28 45 0.36 55.3767 5.9 21 0.51 54.4231 5.89 46 0.44 35.7229 7.67 22 0.51 54.2291 5.64 47 0.44 35.5044 7.68 23 0.51 54.115 5.45 48 0.44 35.4639 7.58 24 0.51 54.2236 5.63 49 0.44 35.9057 7.84 25 0.51 54.4238 5.76 50 0.44 35.7723 7.76
Run#
Packing Time
Avg.Pack pressure
Ram Position at
Max. Filling Run#
Packing Time
Avg.Pack pressure
Ram Position at Max. Filling 51 0.69 43.5531 6.17 76 0.62 48.5678 5.95 52 0.69 43.6576 6.15 77 0.62 48.5768 5.96 53 0.69 43.3827 6.3 78 0.62 48.6114 6.04 54 0.69 43.5057 6.46 79 0.62 48.5403 5.89 55 0.69 43.7166 6.34 80 0.62 48.4619 5.99 56 0.45 49.7937 5.99 81 0.69 53.1015 5.41 57 0.45 49.9634 6.16 82 0.69 53.1207 5.4 58 0.45 49.788 5.91 83 0.69 52.9782 5.33 59 0.45 49.8297 6.02 84 0.69 53.2037 5.5 60 0.45 49.7583 6.2 85 0.69 53.1486 5.52 61 0.53 53.9278 5.76 86 0.75 33.7198 7.34 62 0.53 54.0388 5.65 87 0.75 33.7811 7.35 63 0.53 53.9989 5.82 88 0.75 33.7053 7.1 64 0.53 54.0114 5.88 89 0.75 33.6465 7.07 65 0.53 53.8017 5.75 90 0.75 33.6192 7.25 66 0.6 34.4688 7.26 91 0.8 38.3229 6.5 67 0.6 34.3697 7.12 92 0.8 38.1928 6.92 68 0.6 34.2788 7.14 93 0.8 38.1353 6.63 69 0.6 34.3354 7.26 94 0.8 38.2683 6.6 70 0.6 34.5341 7.36 95 0.8 38.3176 6.8 71 0.66 38.8429 6.78 96 0.57 43.928 6.39 72 0.66 38.772 6.86 97 0.57 43.8874 6.4 73 0.66 38.8504 6.79 98 0.57 44.153 6.34 74 0.66 38.8681 6.81 99 0.57 43.9063 6.28 75 0.66 38.7939 6.8 100 0.57 43.9118 6.41
Run#
Packing Time
Avg.Pack pressure
Ram Position at
Max. Filling Run#
Packing Time
Avg.Pack pressure
Ram Position at Max. Filling 101 0.84 52.5358 5.4 126 0.49 40.6332 6.95 102 0.84 52.619 5.5 127 0.49 40.6783 6.86 103 0.84 52.6375 5.44 128 0.49 40.5516 6.55 104 0.84 52.6518 5.32 129 0.49 40.7806 7.04 105 0.84 52.6287 5.4 130 0.49 40.5748 6.79 106 0.89 33.1921 6.9 131 0.54 41.4207 6.7 107 0.89 33.1932 6.93 132 0.54 41.1186 6.92 108 0.89 33.2905 7.05 133 0.54 41.3964 6.85 109 0.89 33.2298 7.02 134 0.54 41.102 6.9 110 0.89 33.271 7.17 135 0.54 41.1707 6.63 111 0.66 38.6847 6.73 136 0.45 46.5732 6.55 112 0.66 38.7183 6.6 137 0.45 46.5968 6.59 113 0.66 38.734 6.87 138 0.45 46.7584 6.45 114 0.66 38.7164 6.88 139 0.45 46.5697 6.44 115 0.66 38.7074 6.99 140 0.45 46.7527 6.72 116 0.74 43.2098 6.25 141 0.38 47.4762 6.29 117 0.74 43.2437 6.36 142 0.38 47.3339 6.54 118 0.74 43.1538 6.22 143 0.38 47.2937 6.57 119 0.74 43.0615 6.2 144 0.38 47.4619 6.52 120 0.74 43.1046 6.38 145 0.38 47.4768 6.49 121 0.8 47.8252 5.85 146 0.58 38.9999 6.96 122 0.8 47.7129 5.76 147 0.58 39.1321 7.2 123 0.8 47.6991 5.63 148 0.58 39.2269 6.75 124 0.8 47.971 5.88 149 0.58 38.9522 6.81 125 0.8 47.7871 5.56 150 0.58 39.0587 6.87
附錄 D
射出速度SOM 與(射出速度、射出時間、保壓壓力與平均保壓壓力)之多元回 歸分析資料
Run#
Input:
Injection Velocity
Input:
Injection Time
Input:
Packing Pressure
Avg.
Packing pressure
Velocity SOM
1 40 1.3 35 41.3444 13.68000446 2 40 1.3 35 41.5231 13.53078314 3 40 1.3 35 41.4103 13.6253446 4 40 1.3 35 41.0614 13.30482723 5 40 1.3 35 41.5359 13.67609825 6 43 1.3 40 42.372 8.499010091 7 43 1.3 40 42.4303 8.465788875 8 43 1.3 40 42.4854 8.352175775 9 43 1.3 40 42.624 8.460327968 10 43 1.3 40 42.4901 8.495309166 11 46 1.3 45 45.8814 7.431702041 12 46 1.3 45 45.8404 7.429538015 13 46 1.3 45 45.9401 7.585814387 14 46 1.3 45 45.8053 7.268566676 15 46 1.3 45 45.9207 7.28568281 16 49 1.3 50 50.0507 9.938881539 17 49 1.3 50 50.0346 10.36499778 18 49 1.3 50 50.054 10.13759477 19 49 1.3 50 49.8547 10.32220849 20 49 1.3 50 50.1728 10.23955616 21 52 1.3 55 54.4231 14.75815128 22 52 1.3 55 54.2291 14.68509168 23 52 1.3 55 54.115 14.43147129 24 52 1.3 55 54.2236 14.51732561 25 52 1.3 55 54.4238 14.82118336
Run#
Input:
Injection Velocity
Input:
Injection Time
Input:
Packing Pressure
Avg.
Packing pressure
Velocity SOM
26 46 1.4 40 40.2072 6.575423951 27 46 1.4 40 40.3121 6.453117044 28 46 1.4 40 40.1627 6.507766512 29 46 1.4 40 40.3917 6.608436098 30 46 1.4 40 40.4256 6.058604702 31 49 1.4 45 44.4013 9.178522563 32 49 1.4 45 44.5399 9.210358931 33 49 1.4 45 44.5109 9.224111415 34 49 1.4 45 44.2616 8.962327863 35 49 1.4 45 44.4165 9.298474055 36 52 1.4 50 48.9917 13.91064901 37 52 1.4 50 48.8446 14.08670193 38 52 1.4 50 48.8665 14.03527189 39 52 1.4 50 48.7977 14.06352808 40 52 1.4 50 48.9109 13.93589866 41 40 1.4 55 55.3241 14.88365544 42 40 1.4 55 55.2179 15.23660468 43 40 1.4 55 55.5051 14.61288981 44 40 1.4 55 55.3722 14.57762414 45 40 1.4 55 55.3767 14.77585912 46 43 1.4 35 35.7229 7.412941439 47 43 1.4 35 35.5044 7.423913149 48 43 1.4 35 35.4639 7.479377498 49 43 1.4 35 35.9057 7.51718412 50 43 1.4 35 35.7723 7.467113721
Run#
Input:
Injection Velocity
Input:
Injection Time
Input:
Packing Pressure
Avg.
Packing pressure
Velocity SOM
51 52 1.5 45 43.5531 13.76190235 52 52 1.5 45 43.6576 13.73088006 53 52 1.5 45 43.3827 13.85784799 54 52 1.5 45 43.5057 13.89686732 55 52 1.5 45 43.7166 13.78297749 56 40 1.5 50 49.7937 13.78148009 57 40 1.5 50 49.9634 13.60751731 58 40 1.5 50 49.788 13.99633059 59 40 1.5 50 49.8297 13.86245001 60 40 1.5 50 49.7583 13.74768673 61 43 1.5 55 53.9278 9.643286068 62 43 1.5 55 54.0388 9.748356239 63 43 1.5 55 53.9989 9.602368453 64 43 1.5 55 54.0114 9.689337785 65 43 1.5 55 53.8017 9.731261317 66 46 1.5 35 34.4688 6.583562771 67 46 1.5 35 34.3697 6.613088992 68 46 1.5 35 34.2788 6.742279893 69 46 1.5 35 34.3354 6.452856378 70 46 1.5 35 34.5341 6.339500086 71 49 1.5 40 38.8429 9.100506579 72 49 1.5 40 38.772 9.161606503 73 49 1.5 40 38.8504 9.054603946 74 49 1.5 40 38.8681 9.176673888 75 49 1.5 40 38.7939 9.199526712
Run#
Input:
Injection Velocity
Input:
Injection Time
Input:
Packing Pressure
Avg.
Packing pressure
Velocity SOM
76 43 1.6 50 48.5678 9.136335138 77 43 1.6 50 48.5768 9.031834231 78 43 1.6 50 48.6114 8.841461221 79 43 1.6 50 48.5403 8.887834062 80 43 1.6 50 48.4619 9.029503174 81 46 1.6 55 53.1015 8.045636776 82 46 1.6 55 53.1207 7.618984817 83 46 1.6 55 52.9782 7.523942694 84 46 1.6 55 53.2037 7.51416389 85 46 1.6 55 53.1486 7.56379456 86 49 1.6 35 33.7198 8.97474945 87 49 1.6 35 33.7811 9.001716852 88 49 1.6 35 33.7053 8.853776217 89 49 1.6 35 33.6465 8.870533847 90 49 1.6 35 33.6192 8.927999272 91 52 1.6 40 38.3229 13.72914526 92 52 1.6 40 38.1928 13.71081008 93 52 1.6 40 38.1353 13.66206718 94 52 1.6 40 38.2683 13.64677753 95 52 1.6 40 38.3176 13.65370247 96 40 1.6 45 43.928 13.68153515 97 40 1.6 45 43.8874 13.6141425 98 40 1.6 45 44.153 13.81297627 99 40 1.6 45 43.9063 13.7945881 100 40 1.6 45 43.9118 13.75190341
Run#
Input:
Injection Velocity
Input:
Injection Time
Input:
Packing Pressure
Avg.
Packing pressure
Velocity SOM
101 49 1.7 55 52.5358 10.07954805 102 49 1.7 55 52.619 9.905675358 103 49 1.7 55 52.6375 10.01577427 104 49 1.7 55 52.6518 9.843109434 105 49 1.7 55 52.6287 9.982976013 106 52 1.7 35 33.1921 13.75463164 107 52 1.7 35 33.1932 13.65734442 108 52 1.7 35 33.2905 13.7065271 109 52 1.7 35 33.2298 13.60155489 110 52 1.7 35 33.271 13.67337526 111 40 1.7 40 38.6847 13.22912584 112 40 1.7 40 38.7183 13.34324797 113 40 1.7 40 38.734 13.17307716 114 40 1.7 40 38.7164 13.31146683 115 40 1.7 40 38.7074 13.08332706 116 43 1.7 45 43.2098 8.903801801 117 43 1.7 45 43.2437 8.834648536 118 43 1.7 45 43.1538 8.901709225 119 43 1.7 45 43.0615 8.95967628 120 43 1.7 45 43.1046 9.008136783 121 46 1.7 50 47.8252 7.563232207 122 46 1.7 50 47.7129 7.591418344 123 46 1.7 50 47.6991 7.711123564 124 46 1.7 50 47.971 7.588992311 125 46 1.7 50 47.7871 7.672403587
Run#
Input:
Injection Velocity
Input:
Injection Time
Input:
Packing Pressure
Avg.
Packing pressure
Velocity SOM
126 48.6 1.35 40.3 40.6332 8.81511902 127 48.6 1.35 40.3 40.6783 8.899426359 128 48.6 1.35 40.3 40.5516 9.026020143 129 48.6 1.35 40.3 40.7806 9.085118683 130 48.6 1.35 40.3 40.5748 8.875151426 131 47.7 1.41 41.5 41.4207 7.767576288 132 47.7 1.41 41.5 41.1186 7.528686193 133 47.7 1.41 41.5 41.3964 7.838419947 134 47.7 1.41 41.5 41.102 7.808509379 135 47.7 1.41 41.5 41.1707 7.918953439 136 43.6 1.41 46.5 46.5732 8.577461578 137 43.6 1.41 46.5 46.5968 8.401763536 138 43.6 1.41 46.5 46.7584 8.502072583 139 43.6 1.41 46.5 46.5697 8.428454055 140 43.6 1.41 46.5 46.7527 8.616122477 141 42 1.37 46.4 47.4762 10.36345885 142 42 1.37 46.4 47.3339 9.988761078 143 42 1.37 46.4 47.2937 10.26490864 144 42 1.37 46.4 47.4619 10.20624784 145 42 1.37 46.4 47.4768 10.15019647 146 40.3 1.62 39.9 38.9999 12.68708453 147 40.3 1.62 39.9 39.1321 12.76602365 148 40.3 1.62 39.9 39.2269 12.81828028 149 40.3 1.62 39.9 38.9522 12.8277736 150 40.3 1.62 39.9 39.0587 12.7313333
附錄 E
螺桿位置曲線SOM 與(射出時間、保壓時間、充填保壓切換點與螺桿最大行 程)之多元回歸分析資料
Run#
Input:
Injection Time
Packing Time
VP Switch Position
Ram Position at Max.
Filling
Position SOM
1 1.3 0.25 7.49 7.09 16.12692008 2 1.3 0.25 7.49 7.09 15.9244381 3 1.3 0.25 7.49 7.1 16.11543771 4 1.3 0.25 7.49 7.09 15.15822872 5 1.3 0.25 7.49 7.08 15.93716739 6 1.3 0.33 7.7 6.96 8.113173029 7 1.3 0.33 7.7 6.93 8.04331955 8 1.3 0.33 7.69 6.94 8.068587619 9 1.3 0.33 7.7 7.06 8.818419379 10 1.3 0.33 7.69 7.09 9.112780952 11 1.3 0.39 7.89 6.48 5.297268535 12 1.3 0.39 7.89 6.66 6.054113267 13 1.3 0.39 7.89 6.74 7.00600589 14 1.3 0.39 7.89 6.54 5.310089984 15 1.3 0.39 7.9 6.47 5.132244327 16 1.3 0.45 8.09 6.17 8.535121168 17 1.3 0.45 8.1 6.57 9.506167464 18 1.3 0.45 8.1 6.12 8.558429453 19 1.3 0.45 8.09 6.2 8.878314319 20 1.3 0.45 8.09 6.28 9.089114737 21 1.3 0.51 8.29 5.89 13.97913172 22 1.3 0.51 8.29 5.64 14.13096769 23 1.3 0.51 8.29 5.45 14.64228608 24 1.3 0.51 8.3 5.63 14.01738524 25 1.3 0.51 8.3 5.76 14.46505881
Run#
Input:
Injection Time
Packing Time
VP Switch Position
Ram Position at Max.
Filling
Position SOM
26 1.4 0.48 7.49 6.93 4.566037677 27 1.4 0.48 7.49 6.82 3.892431115 28 1.4 0.48 7.49 6.95 4.563646303 29 1.4 0.49 7.49 7.06 5.620556572 30 1.4 0.49 7.5 6.88 4.008306965 31 1.4 0.54 7.69 6.32 7.691165638 32 1.4 0.55 7.69 6.45 7.673200825 33 1.4 0.54 7.69 6.39 7.544763205 34 1.4 0.54 7.69 6.04 8.69585108 35 1.4 0.54 7.69 6.61 7.800381221 36 1.4 0.6 7.9 5.85 13.73229203 37 1.4 0.6 7.89 6.1 13.15034931 38 1.4 0.6 7.89 6.03 13.23532745 39 1.4 0.6 7.89 5.98 13.39790916 40 1.4 0.6 7.89 5.97 13.51405001 41 1.4 0.36 8.1 5.82 12.964831 42 1.4 0.36 8.09 5.63 13.36103939 43 1.4 0.36 8.1 6.02 12.80676812 44 1.4 0.36 8.09 6.01 12.86030396 45 1.4 0.36 8.1 5.9 12.93476221 46 1.4 0.44 8.3 7.67 10.56200825 47 1.4 0.44 8.29 7.68 10.81196253 48 1.4 0.44 8.29 7.58 10.17709492 49 1.4 0.44 8.29 7.84 11.76417839 50 1.4 0.44 8.3 7.76 11.0553493
Run#
Input:
Injection Time
Packing Time
VP Switch Position
Ram Position at Max.
Filling
Position SOM
51 1.5 0.69 7.49 6.17 13.23149519 52 1.5 0.69 7.49 6.15 13.33714072 53 1.5 0.69 7.49 6.3 12.94019006 54 1.5 0.69 7.49 6.46 12.71541232 55 1.5 0.69 7.49 6.34 12.85805345 56 1.5 0.45 7.69 5.99 13.06717204 57 1.5 0.45 7.69 6.16 12.79816386 58 1.5 0.45 7.69 5.91 13.16866092 59 1.5 0.45 7.69 6.02 13.00801174 60 1.5 0.45 7.69 6.2 12.78838276 61 1.5 0.53 7.9 5.76 7.909098145 62 1.5 0.53 7.9 5.65 8.351579935 63 1.5 0.53 7.89 5.82 7.614774183 64 1.5 0.53 7.9 5.88 7.393885821 65 1.5 0.53 7.9 5.75 8.063709827 66 1.5 0.6 8.09 7.26 5.920515574 67 1.5 0.6 8.09 7.12 5.51122399 68 1.5 0.6 8.1 7.14 5.523577809 69 1.5 0.6 8.09 7.26 5.657665645 70 1.5 0.6 8.09 7.36 6.405539935 71 1.5 0.66 8.3 6.78 7.149653672 72 1.5 0.66 8.29 6.86 7.173243443 73 1.5 0.66 8.29 6.79 7.1104743 74 1.5 0.66 8.29 6.81 7.099020357 75 1.5 0.66 8.3 6.8 7.02688455
Run#
Input:
Injection Time
Packing Time
VP Switch Position
Ram Position at Max.
Filling
Position SOM
76 1.6 0.62 7.49 5.95 8.046609588 77 1.6 0.62 7.5 5.96 8.089972528 78 1.6 0.62 7.5 6.04 7.405239537 79 1.6 0.62 7.49 5.89 8.269936439 80 1.6 0.62 7.5 5.99 7.72534848 81 1.6 0.69 7.69 5.41 10.72298411 82 1.6 0.69 7.69 5.4 10.51123045 83 1.6 0.69 7.69 5.33 11.22586972 84 1.6 0.69 7.69 5.5 9.888636694 85 1.6 0.69 7.69 5.52 9.52185729 86 1.6 0.75 7.89 7.34 8.436995951 87 1.6 0.75 7.9 7.35 8.372990593 88 1.6 0.75 7.89 7.1 7.582003872 89 1.6 0.75 7.89 7.07 7.732762997 90 1.6 0.75 7.9 7.25 7.976569187 91 1.6 0.8 8.09 6.5 12.48985283 92 1.6 0.8 8.1 6.92 12.17082898 93 1.6 0.8 8.1 6.63 12.19964531 94 1.6 0.8 8.1 6.6 12.26296874 95 1.6 0.8 8.09 6.8 12.12512688 96 1.6 0.57 8.3 6.39 13.23898624 97 1.6 0.57 8.3 6.4 13.35064675 98 1.6 0.57 8.3 6.34 13.38720052 99 1.6 0.57 8.3 6.28 13.35587611 100 1.6 0.57 8.3 6.41 13.25192696
Run#
Input:
Injection Time
Packing Time
VP Switch Position
Ram Position at Max.
Filling
Position SOM
101 1.7 0.84 7.5 5.4 13.99629933 102 1.7 0.84 7.5 5.5 13.32809059 103 1.7 0.84 7.5 5.44 13.71414176 104 1.7 0.84 7.5 5.32 14.86705738 105 1.7 0.84 7.5 5.4 14.15144127 106 1.7 0.89 7.69 6.9 12.44464984 107 1.7 0.89 7.69 6.93 12.4780815 108 1.7 0.89 7.69 7.05 12.44675337 109 1.7 0.89 7.69 7.02 12.48399059 110 1.7 0.89 7.7 7.17 12.41683854 111 1.7 0.66 7.89 6.73 13.09853121 112 1.7 0.66 7.89 6.6 13.47350222 113 1.7 0.66 7.9 6.87 13.20460395 114 1.7 0.66 7.89 6.88 13.11679124 115 1.7 0.66 7.89 6.99 13.22343589 116 1.7 0.74 8.1 6.25 7.746682833 117 1.7 0.74 8.1 6.36 7.25039545 118 1.7 0.74 8.09 6.22 7.995689889 119 1.7 0.74 8.1 6.2 7.911426598 120 1.7 0.74 8.09 6.38 7.354414459 121 1.7 0.8 8.29 5.85 8.123011223 122 1.7 0.8 8.29 5.76 8.778179232 123 1.7 0.8 8.3 5.63 9.669635293 124 1.7 0.8 8.29 5.88 7.749149867 125 1.7 0.8 8.29 5.56 10.29705927