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研究生:陳睿煬
研究生(外文):Rui-Yang Chen
論文名稱:短玻璃纖維與聚四氟乙烯強化聚碳酸酯複合材料之介電性質最佳混合比研究
論文名稱(外文):A Study of Dielectric properties of Short Glass Fiber and Polytetrafluoroethylene Reinforced Polycarbonate Composites
指導教授:楊永光楊永光引用關係
指導教授(外文):Yung-Kuang Yang
學位類別:碩士
校院名稱:明新科技大學
系所名稱:精密機電工程研究所
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:2011
畢業學年度:100
語文別:中文
論文頁數:88
中文關鍵詞:混合型實驗反應曲面法倒傳遞類神經網路基因演算法拉伸強度介電性質
外文關鍵詞:Mixture designRSMArtificial neural networkGenetic algorithmTensile strengthDielectric Properties
相關次數:
  • 被引用被引用:5
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本研究以短玻璃纖維(Short Glass Fibers,SGF)含量 10-20%與聚四氟乙烯(Polytetrafluoroethylene,PTFE)含量4-12%的成份強化聚碳酸酯(Polycarbonate,PC) 含量 68-86%,應用Design-Expert產生D最佳設計(D-optimal)的技術方法規劃實驗,即採用混合型實驗模式。分別應用反應曲面法(Response Surface Method, RSM)與倒傳遞類神經網路(Back-Propagation Neural Network, BPNN)搭配基因演算法(Genetic Algorithm, GA)兩種方法進行數據分析;研討射出試件的拉伸強度(Tensile Strength)、介電常數(Permittivity)及介電強度(Dielectric Strength)對混合比例與塑料之關係。
本研究首先藉由變異數分析(Analysis of Variance,ANOVA),完成PC與SGF和PTFE等原塑料對介電性質與機械強度的影響程度,同時應用迴歸分析技巧,建構複合材料脂含量比例與介電性質和機械性質的數學模式。類神經網路則是藉由訓練類神經網路進行實驗的預測,將訓練的神經網路搭配基因演算法進行最佳化製程參數組合預測,最後也完成基因演算法所預測參數組合再輸入到類神經網路進行預測。
研究結果為RSM之最佳混合比例為聚碳酸酯0.72、短玻璃纖維0.20、聚四氟乙烯0.08;GA之最佳混合比例為聚碳酸酯0.73、短玻璃纖維0.20、聚四氟乙烯0.07時有最佳的介電性質及拉伸強度,最後驗證兩種方法所獲得之最佳化製程參數組合的預測結果,發現類神經網路搭配基因演算法(GA)稍微優於反應曲面法(RSM)。
This paper applies Design-Expert to generate the technology of D-optimal mixture design which integrating response surface methodology(RSM), and back-propagation neural network integrae genetic algorithm (BPNN/GA) method separately to discuss variation of the permittivity, dielectric strength, and tensile strength depended on injection molding mixture ratio of 10-20% short glass fiber (SGF) 4-12% polytetrafluoroethylene (PTFE) and 68-86% reinforced polycarbonate (PC) composites.
The analysis of variance (ANOVA) was applied to identify the effect of mixture ratio of SGF and PTFE reinforced PC composites for the permittivity and dielectric strength and tensile strength.By regression analysis, a mathematical predictive model ofthe permittivity and dielectric strength and tensile strength were developed in terms of the mixture ratio setting. The combining BPNN/GA optimization method can be obtained for the appropriate combinations of the optimal mixture ratio setting. In addition, the result of BPNN integrating GA was also predictive with BPNN approach.
The results show that the optimal mixture ratio setting gives appropriate combinations with a PC of 0.72, a SGF of 0.20, and a PTFE of 0.08 by RSM approach. Additionally, BPNN/GA approaches are gives appropriate combinations with a PC of 0.73, a SGF of 0.20, and a PTFE of 0.07. By verification results show the proposed algorithm of GA approach has better prediction result than the RSM method.
摘 要..................................................... i
Abstract................................................. ii
誌 謝................................................... iii
目 錄.................................................... iv
表目錄.................................................... vi
圖目錄................................................... vii
第一章 緒論 ............................................... 1
1.1 研究目的與動機.......................................... 1
1.2 文獻探討............................................... 3
1.3 本文架構............................................... 7
第二章 研究理論 ............................................ 9
2.1 射出成型加工法 ......................................... 9
2.1.1 塑料性質簡介.......................................... 9
2.1.2 射出機組件.......................................... 10
2.1.3 射出成型流道系統 ..................................... 11
2.1.4 射出成形週期 ........................................ 11
2.2 反應曲面法 ........................................... 13
2.2.1混合型實驗........................................... 15
2.2.2 統計檢定理論 ........................................ 16
2.2.3 變異數分析 ......................................... 17
2.2.4 迴歸分析 .......................................... 19
2.2.5 殘差分析 .......................................... 20
2.2.6 期望函數 .......................................... 21
2.3 類神經網路 .......................................... 23
2.3.1 類神經網路之優點.................................... 24
2.3.2 類神經網路結合基因演算法步驟 ......................... 25
2.3.3 生物神經元模型 ..................................... 31
2.3.4 人工神經元模型...................................... 32
2.3.5 類神經網路系統架構................................... 33
2.3.6 轉移函數........................................... 34
2.3.7 倒傳遞神經網路架構................................... 36
2.3.8 倒傳遞網路之學習演算法................................ 37
2.4 基因演算法 ........................................... 41
2.4.1 基因演算法演算流程................................... 41
2.4.2 複製.............................................. 42
2.4.3 交配.............................................. 43
2.4.4 交配機率........................................... 43
2.4.5 突變.............................................. 44
2.4.6 突變機率........................................... 44
2.4.7 適應函數........................................... 44
2.4.8 編碼與字串長度...................................... 45
2.4.9 終止條件........................................... 45
第三章 實驗規劃........................................... 46
3.1 定義目標............................................. 47
3.2 射出成型機及射出塑料................................... 47
3.3 實驗及量測........................................... 48
3.3.1 拉伸強度試驗....................................... 48
3.3.2 電容率試驗......................................... 48
3.3.3 介電強度試驗....................................... 49
3.4 實驗數據分析......................................... 49
3.4.1 反應曲面法......................................... 50
3.4.2 類神經網路搭配基因演算法 ............................ 51
第四章 實驗結果與分析..................................... 53
4.1 反應曲面法結果 ...................................... 53
4.1.1 變異數分析 ....................................... 54
4.1.2 迴歸模型建構 ...................................... 56
4.1.3 檢驗模型適當性 .................................... 57
4.1.4 最佳化分析 ....................................... 68
4.2 類神經網路預測結果 ................................... 70
4.3 基因演算法最佳化預測結果............................... 70
第五章 結論與未來展望..................................... 71
5.1 結論............................................... 71
5.2 未來展望 ........................................... 72
參考文獻................................................ 73
附錄A達到95%信心水準之最小F值 ............................. 77
作者簡介................................................ 78
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