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研究生： 曾仁二
Jen-Erh Tseng
論文名稱： 聚對苯二甲酸乙二酯複合奈米二氧化鈦顆粒於熔融紡絲加工參數最佳化之研究
The Research on Melt Spinning Process Parameter Optimizing with PET Composite Titanium Dioxide Nanoparticles
指導教授： 郭中豐
Chung-Feng Jeffrey Kuo
口試委員: 黃昌群
Chang-Chiun Huang
蘇德利
Te-Li Su
高志遠
Chih-Yuan Kao
邱錦勳
Chin-Hsun Chiu
學位類別： 碩士
Master
系所名稱： 工程學院 - 材料科學與工程系
Department of Materials Science and Engineering
論文出版年： 2011
畢業學年度： 98
語文別： 中文
論文頁數： 108
中文關鍵詞： 熔融紡絲 、田口法 、資料包絡法 、主成份分析 、倒傳遞類神經網路
外文關鍵詞： melt spinning, Taguchi Method, data envelopment analysis, principal component analysis, back propagation neural network
相關次數： 點閱：458  下載：3 
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    • 本文探討藉由聚對苯二甲酸乙二酯(PET)與二氧化鈦(TiO2)奈米微粒之參混(Blending)來製造出具有含二氧化鈦之功能性纖維。實驗發現，隨著纖維之牽伸速度增加，浮現在纖維表面之二氧化鈦聚集顆粒會更明顯，由掃描式電子顯微鏡觀察得知，纖維之二氧化鈦顆粒會分佈在纖維與空氣的界面，即可發揮光觸媒之效果。
    製程參數為影響其機械性能之關鍵，影響熔融紡絲性能的關鍵製程參數為三段套筒溫度、模頭溫度、齒輪幫浦溫度、紡嘴溫度以及牽伸捲取速度等七種，品質特性為最大抗拉強度、伸長率以及回復模數。本文使用田口方法中的直交表規劃實驗，實驗完成後所得的各品質數據，透過田口方法的主效果分析與變異數分析理論得到對單一品質之最佳參數，將實驗所得到的各品質數據，再以資料包絡分析法(Data Envelopment Analysis, DEA)以及主成份分析(Principal Component Analysis, PCA)找出最佳製程參數水準之組合，最後透過田口確認實驗與計算信賴區間，並應用倒傳遞類神經網路建構熔融紡絲製程參數之預測系統，經過網路學習訓練，預測誤差率在5%以內。

    This study attempted to produce functional fiber containing TiO2 by blending PET with TiO2 nanoparticles. The experiment showed that the TiO2 aggregation particles on the fiber surface become more obvious as the draw speed of fiber increases, as observed through SEM. The TiO2 particles of fiber are distributed at the interface between fiber and air.
    The process parameters are the keys influencing the mechanical properties. The key process parameters influencing the performance of melt spinning are three-stage sleeve temperature, diehead temperature, gear pump temperature, spinneret temperature and coiling speed. The quality characteristics are ultimate tensile strength, elongation at fracture and modulus of resilience. This study used the orthogonal of Taguchi Method to design the experiment. The optimum parameter for single quality was obtained from the qualitative data derived from the experiment by using factor effects and analysis of variance (ANOVA) theory of Taguchi Method. The optimum process parameter level combination was determined from the qualitative data obtained from the experiment by using Data Envelopment Analysis (DEA) and principal component analysis (PCA). Finally, the melt spinning process parameter prediction system was constructed by using back propagation neural network (BPNN) through Taguchi confirmation experiment and calculation of confidence interval. The prediction error rate was less than 5% after network learning and training.

    摘要 I ABSTRACT II 誌謝 III 目錄 IV 圖索引 VIII 表索引 XI 第1章 緒論 1 1.1 前言 1 1.2 研究動機與目的 2 1.3 文獻回顧 3 1.3.1 PET複合二氧化鈦 3 1.3.2 製程最佳化 5 1.3.3 預測系統 7 1.4 論文大綱 9 1.5 研究流程 10 第2章 實驗儀器介紹 11 2.1 熔融紡絲 11 2.2 材料分析 12 2.2.1 熱重損失分析儀 14 2.2.2 熱示差分析儀 15 2.2.3 掃瞄式電子顯微鏡 15 2.2.4 能量分散光譜儀 17 2.2.5 多功能萬能材料試驗機 18 2.3 拉伸試驗 19 第3章 製程最佳化相關理論 21 3.1 田口方法 21 3.1.1 田口方法概述 22 3.1.2 實驗因子簡述 25 3.1.3 直交表 26 3.1.4 直交表的選擇 28 3.1.5 品質損失函數 28 3.1.6 訊號雜訊比 29 3.1.7 主效果分析 30 3.1.8 變異數分析 31 3.1.9 信賴區間 34 3.2 資料包絡分析 36 3.2.1 資料包絡分析概論 36 3.2.2 相對效率法 37 3.2.3 資料包絡分析排序法 39 3.3 主成份分析 40 3.3.1 主成份分析概論 41 3.3.2 計算理論基礎 41 3.3.3 主成份計算步驟 44 第4章 類神經網路理論 47 4.1 類神經網路概論 47 4.2 倒傳遞神經網路之架構 52 4.3 倒傳遞類神經網路之參數 53 4.4 倒傳遞類神經網路之計算流程 55 4.5 LEVENBERG-MARQUARDT演算法 59 第5章 實驗步驟與規劃 61 5.1 實驗材料 61 5.2 材料分析 62 5.3 實驗規劃 70 5.4 實驗設備 72 5.5 檢測程序 73 第6章 實驗結果與討論 74 6.1 熔融紡絲實驗數據 74 6.1.1 伸長率實驗數據分析 74 6.1.2 最大抗拉強度實驗數據 78 6.1.3 回復模數實驗數據分析 82 6.2 多品質特性分析 86 6.2.1 資料包絡分析 86 6.2.2 主成份分析 90 6.2.3 S/N比加法模式數值化運算 95 6.3 確認實驗 97 6.4 預測系統 98 6.4.1 數據正規化 98 6.4.2 構建預測系統 100 6.4.3 驗證預測效果 101 第7章 結論 103 參考文獻 105

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