一般常用最佳化方法在塑膠射出成型之應用｜國立臺灣科技大學博碩士論文系統

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研究生：	林啟湶 Chi-Chuan Lin
論文名稱：	一般常用最佳化方法在塑膠射出成型之應用 Application of Common Optimization Methods Used in Plastic Injection Molding
指導教授：	陳恩宗 En-Tsung Chen
口試委員:	湯同達 Tongdar Tang 洪俊卿 Jin-Tsing Hong 陳炤彰 C.-C. A. Chen
學位類別：	碩士 Master
系所名稱：	工程學院 - 機械工程系 Department of Mechanical Engineering
論文出版年：	2005
畢業學年度：	93
語文別：	中文
論文頁數：	108
中文關鍵詞：	田口法、基因演算法、射出成型、類神經網路
外文關鍵詞：	Taguchi method, genetic algorithm, injection molding, artificial neural network
相關次數：	點閱：416 下載：62
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塑膠射出成型是目前最常使用的成型技術。在進行塑膠射出成型，為獲得最佳的塑件品質除了良好的模具設計外，尚需考慮適當的製程參數；傳統上它係依靠有經驗的技術人員，利用試誤法來找出較佳的參數組合，但此方式通常需耗費大量的時間與金錢。因此，本文提出以不同演算法則，來探討製程參數組合的最佳化。
本文以模溫、融溫、射出時間、保壓壓力、保壓時間為控制因子，首先分別以基因演算法和田口法配合Moldflow模擬分析來搜尋最佳參數組合，以期達到塑件收縮最小與壁面剪應力最小化。而後再進一步將基因演算法結合類神經網路進行參數最佳化，以縮短最佳化時程，另外將田口法結合類神經網路，微調參數組合進行最佳化。就本文所探討的塑件與要求的品質而言，以田口法結合類神經網路是最具優勢與效益的最佳化方法。

At present, the injection molding process is the most popular plastic processing method. In order to get plastic parts with good qualities, good mold design and suitable processing parameters are the preliminary requirements. In tradition, the technician must use trial and error method to search the optimal injection molding parameters, which will get the cost and the time required to increase remarkably. This paper uses several different algorithms to find optimal injection molding processing parameters.
Melt temperature, mold temperature, injection time, packing time, and packing pressure are chosen as the control factors. First, MPI 4.1 software is used to cooperate with the genetic algorithm and the Taguchi method respectively to search optimum parameters to get the lowest shrinkage and the wall shear stress. Then hybrid of the genetic algorithm and the artificial neural network is used to find the optimal parameters with the benefit of further reducing the time required. On the other hand, the Taguchi method and the artificial neural network are coupled by tuning the optimum combination obtained in the Taguchi method to find the best injection molding processing parameters. The results shown that combining the Taguchi method with the artificial neural network is the best method in this study.

中文摘要..........................................i
英文摘要.........................................ii
致謝............................................iii
目錄.............................................iv
圖表索引.........................................ix
第一章  緒論......................................1
1  前言.........................................1
2  研究動機及目標...............................3
3  文獻回顧.....................................4
第二章  遺傳基因演算法............................7
1  前言.........................................7
2  基本概念.....................................8
2.1  個體.......................................8
2.2  族群.......................................8
2.3  適應函數...................................9
2.4  適應值....................................10
2.5  平均適應值................................10
2.6  編碼與解碼................................11
2.7  複製......................................12
2.8  交配......................................13
2.9  突變......................................14
2.10  保留最佳基因.............................15
2.11  遺傳基因演算法的收斂性...................15
第三章  田口品質工程.............................16
1  前言........................................16
2  田口式品質工程簡介..........................16
3  田口實驗法步驟..............................17
4  品質特性的種類..............................17
4.1  望小特性..................................18
4.2  望大特性..................................18
4.3  望目特性..................................19
5  直交表......................................19
第四章  類神經網路...............................21
1  前言........................................21
2  生物神經網路................................22
3  類神經網路..................................24
3.1  人工神經元之模型..........................24
3.2  類神經網路之基本架構......................26
3.3  回饋式網路的架構..........................26
3.4  前饋式網路的架構..........................27
3.5  轉移函數..................................28
3.6  類神經網路的演算..........................30
3.7  學習規則..................................30
4  倒傳遞類神經網路............................31
4.1  神經元模型................................31
4.2  倒傳遞網路的架構..........................32
4.3  轉移函數..................................32
4.4  網路訓練..................................32
5  類神經網路的優點............................33
第五章  模流分析.................................35
1  電腦輔助工程分析............................35
2  前處理......................................36
2.1  幾何模型之建立............................36
2.2  網格種類..................................37
2.3  材料選擇..................................37
2.4  模擬之設定................................38
3  分析形式....................................38
3.1  流動分析..................................38
3.2  冷卻分析..................................39
3.3  翹曲分析..................................39
4  後處理......................................40
第六章  參數設定與最佳化方法.....................41
1  模擬分析與設定..............................41
1.1  收縮與剪應力之定義與測量..................41
1.2  任意參數組合對收縮與最大剪應力之影響......42
2  遺傳基因演算法參數最佳化....................42
2.1  定義目標函數與適應函數....................42
2.2  遺傳基因演算法之製程參數..................43
3  田口法參數最佳化............................44
4  遺傳基因演算法結合類神經網路之參數最佳化....44
5  田口法結合類神經網路之參數最佳化............45
第七章  結果與討論...............................46
1  最佳化參數組合..............................46
1.1  遺傳基因演算之最佳化參數組合..............46
1.2  基因演算結合類神經網路之最佳化參數組合....46
1.3  田口法之最佳化參數組合....................47
1.4  田口法結合類神經網路之最佳化參數組合......48
1.5  各種最佳化方法結果之比較..................49
2  參數組合對塑件品質之影響....................50
2.1  塑件收縮與翹曲之探討......................50
2.2  塑件壁面最大剪應力之探討..................51
2.3  最佳化參數組合之塑件品質..................52
第八章  結論與建議...............................53
1  結論........................................53
2  建議........................................54
參考文獻.........................................90
附錄A:射出成型之理論背景.........................95
附錄B:類神經網路之數學模型......................100
作者簡介........................................108

                                

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