第五章 結論與未來研究建議
5.1 結論
變異（穩健性）製 是基於目標之最 佳化理論，故無變異之考量。然而兩方法參數設計下的製程精密度指標均達
範圍。
口方法結合DFP 演算法提供機構 件射出成形產業一新程序方式，減少了傳統製程參數設定前大量的試模調整 於使用傳統全因子實驗加上二次實驗以確認最後最佳製程參數設 數值運算量，另 演算所得之最佳製程參數設定下的單一品質特性亦非常符合業者之規格要
5.2
由於射出成形製程充滿不確定性，經常因外在環境因素之影響而有所改
以及
可再考慮外在環境因素對製程參數之影響性。亦或可運用工程學上之控制器 方法，針對製程上不易控制且不易發現之干擾因素，進行製程之調整以及預
程之觀點有關，而DFP 演算法其演算過程
到第一級指標之標準，顯示兩製程參數設計下之品質特性均達到極小變異之
依據本研究之研究目的，本研究運用田
作業，且優
計之步驟，並且大量減化了運用神經網路以及基因演算法之
求。
未來研究建議
變，例如：季節因子之變動，易使室內溫度改變，進而影響材料熔化之程度 射出模件之冷卻時間，而使產品品質產生變化。因此在未來相關研究上
測，使得產品品質能更準確地符合業者所期望之目標值範圍。
參考文獻
洪瑞庭 ，「塑膠加工技術與工程」，高立圖書有限公司。
1. (2000)
2. 洪宗仁(2002)，「應用柔性演算法於吹塑成形件之製程與設計最佳化」，
台灣科技大學機械工程系碩士論文。
3.
論文。
7. 陳相弦(2000)，「使用柔性演算法於穩健最佳化設計」，台灣科技大學機
8.
范樂陽(1987)，「塑膠加工實務」，高立圖書有限公司。
4. 張榮語(1995)，「射出成形模具設計:模具設計」，高立圖書有限公司。
5. 張旭華(1999)，「運用柔性演算法求解最佳參數設計」，交通大學工業工 程與管理系碩士論文。
6. 陳璟忠(2000)，「射出成形機安全性能探討」，中興大學機械工程學系碩 士
械工程系碩士論文。
黃臣鴻(2003)，「PC/ABS 合膠機械性質之射出成形條件最佳化」，中 央大學機械工程研究所博士論文。
9. 財團法人塑膠工業技術發展中心，http://w3.pidc.org.tw/rd/default.aspx。
10. 塑 膠 e 學 院 網 站 (2002) ，「 射 出 加 工 系 列 - 射 出 機 原 理 與 結 構 」，
http://psdn.pidc.org.tw/。
11. 傅和彥、黃士滔(2001)，「品質管理」，前程企業圖書有限公司。
12. Arora, J. S.(1989), Introduction to Optimum Design, McGraw-Hill.
iang, K. Ta. and Chang, F. P. (2006), “Application of grey-fuzzy logic on the optimal process design of an injection-molded part with a thin shell feature”, International Communications in Heat and Mass Transfer, Vol. 33, Iss. 1, pp. 94-101.
14. Covas, J. A., A. G.. Cunha and Oliveira, P.(1999), “An optimization Approach to Practical Problem in Plasticating Single Screw Extrusion”, Polymer Engineering and Science,Vol. 39, lss. 3, pp.443-454.
15. Chen, F. C. and Tzeng, Y. F.(2004), “Optimization of the volumetric accuracy of high-speed computer numerical control milling with dynamic quality characteristics”, Proceedings of the Institution of Mechanical 13. Ch
Engineers, Vol. 218, Iss. 12, p
16. Chou, C. Y., Chen, C. H., Yang, C. C., Wu, C. C.(2005), “Application of s of a turning p ”, International Journal of Production Research, Vol. 43, Iss.
Davis, C. C. and Husdonm, J. C.(1991), “Proceedings of SPE Annual Technical Conference, Vol. 49, pp. 474.
problem of an injection- olded part with a thin shell feature”, Journal of
19. tterson, W. I.(1996), “Control of Part Weight in Injection Molding of Amorphous Thermoplastics”, Polymer
and Experimental Studies of Anisotropic Shrinkage in Injection Moldings of
21. . and Erzurumlu, T.(2005), “Warpage optimization
. 2, pp.
22.
, Vol. 44, Iss. 5, pp.917-929.
p. 1741-1755.
bivariate parameter design to the optimization of the operating condition rocess
24, pp. 5229-5235.
17.
18. Huang, M. C. and Tai, C. C.(2001), “The effective factors in the warpage m
Materials Processing Technology, Vol. 110, Iss. 1, pp.1-9.
Karmal, M. R., Varela, A. E. and Pa
Engineering and Science, Vol. 39,No.5,pp.940-952.
20. Keehae, K., Isayev, A. I., Kim, K. H. and Sweden, C. V.(2006), ”Theoretical
Semicrystalline Polymers”, Polymer Engineering and Science, Vol. 46, Iss. 6;
pp.712 -729.
Kurtaran, H., Ozcelik, B
of a bus ceiling lamp base using neural network model and genetic algorithm”, Journal of Materials Processing Tech, Vol. 169, Iss
314-319.
Liao, S. J., Chang, D. Y., Chen, H. J. and Tsou, L. S.(2004), “Optimal Process Conditions of Shrinkage and Warpage of Thin-Wall Parts”, Polymer Engineering and Science
23. Liu, J. L.(2006), “Novel Taguchi-Simulated Annealing Method Applied to Airfoil and Wing Planform Optimization”, Journal of Aircraft, Vol. 43, Iss. 1, pp. 102-112.
24. 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
25. Muzammil, M., Singh, P. P. and Faisal, T.(2003), “Optimization of gear blank casting process by using Taguchi's Robust Design technique”, Quality Engineering, Vol. 15, Iss. 3, pp. 351-362.
Mok, S. L. and K
26. wong, C. K.(2002), “Application of artificial neural
Intelligent Manufacturing, Vol. 13,
27.
rnational Journal of
29.
30. 05), “ Fuzzy logic optimization of injection molding of liquid
31.
32. H. and Liang, W. J.(2005), “Effects of Geometry and
33. nd-Conquer Parallel
34. Furong, G.(2006), “Injection molding product weight：Online network and fuzzy logic in a case-based system for initial process parameter setting of injection molding”, Journal of
Iss. 3, pp. 165-176.
Phadke, M. S.(1989), “Quality Engineering Using Robust Design, Prentice-Hall.
28. Shang, J. S., Li, S. and Tadikamalla, P.(2004), “Operational design of a supply chain system using the Taguchi method, response surface methodology, simulation, and optimization” Inte
Production Research, Vol. 42, Iss. 18, pp. 3823-3833.
Sanschagrin, B.(1983), “Process Control of Injection Molding”, Polymer Engineering and Science, Vol. 23, No. 8, pp.431-438.
Sillo, F. Z.(20
silicone rubber”, University of Massachusetts Lowell.
Taguchi, G.., Chowdhury, S. and Wu, Y.(2004), “Taguchi’s Quality Enginneering Handbook”, John Wiley and Sons.
Wu, C.
Injection-Molding Parameters on Weld-Line Strength”, Polymer Engineering and Science, Vol. 45, No. 7, pp. 1021-1031
Wang, N. and Chen, L. W.(2005), “A Divide-a
Computing Scheme for the Optimization Analysis of Tribological Systems”, Tribology & Lubrication Technology, Vol. 61, Iss. 1, pp. 38-47.
Yang, Y. and
prediction control based on a nonlinear principal component regression model”, Polymer Engineering and Science, Vol.46, No.4, pp.540-548.
Thermoplastic Injection Molding”, Polymer Engineering and Science, Vol.39, No.9, pp.1787-1801.
附錄 A
田口實驗最佳製程參數實驗數據表
次數 實驗數據(g) 次數 實驗數據(g) 1 10.6211 25 10.6193 2 10.6205 26 10.6222 3 10.6183 27 10.6238 4 10.625 28 10.6227 5 10.624 29 10.6235 6 10.6266 30 10.6236 7 10.6212 31 10.6228 8 10.6221 32 10.6276 9 10.6263 33 10.6255 10 10.6242 34 10.6246 11 10.6243 35 10.6223 12 10.6211 36 10.6223 13 10.6217 37 10.6205 14 10.6237 38 10.6223 15 10.6234 39 10.6228 16 10.6213 40 10.6231 17 10.6253 41 10.6212 18 10.6231 42 10.6215 19 10.6209 43 10.6182 20 10.622 44 10.617 21 10.6203 45 10.6252 22 10.616 46 10.616 23 10.6231 47 10.6198 24 10.6222 48 10.621
Avg. 10.6222
|Std.| 0.0025
平均差值 0.0422