臺灣博碩士論文加值系統

在抗暖化聲勢如日中天之際，越來越多國家政府競相參與替代能源的研究發展。工業技術研究院團隊已對太陽能選擇性吸收膜濺鍍製程進行技術開發，在批次濺鍍技術的基礎上，建置國內首座捲對捲（Roll-to-Roll）連續式濺鍍製程原型機，並希望結合工業工程領域之相關知識，最佳化製程參數。
太陽能選擇性吸收膜連續濺鍍製程中，影響其成品品質的製程參數眾多，如果僅依據工程師經驗來判斷其設定值，可能導致製程不穩定而造成不良率提高。本研究針對太陽能選擇性吸收膜製程參數最佳化提出一系統性的求解程序，首先以歷史資料與工程知識篩選出重要參數，使用田口方法（Taguchi Methods）求取最佳化參數組合，另外也透過倒傳遞類神經網路（Back-Propagation Neural Network）建立回應值之SN比與控制因子間的關係模式，以期望函數（Desirability Function）合併輸出值作為適應函數，再利用基因演算法（Genetic Algorithms）求解最佳製程參數水準組合。
經由實驗驗證結果得知，本研究提出之方法能有效掌控問題核心，僅透過18筆資料與調整七個關鍵因子，就能使績效指標提升至國際級高品質標準內。能源節約效益相當於每年可抑制二氧化碳排放達27,553公噸，這是傳統烤漆技術的11倍之多，站在工業界的角度儼然是十分可觀的數字。

“Anti-Global Warming” is a hot topic around the world. A growing number of countries have participated in the research of alternative energies. Industrial Technology Research Institute of Taiwan (ITRT) has already developed the technology in solar energy selective absorption film continuous sputtering process. The institute established the first Roll-to-Roll continuous sputtering process machine in Taiwan based on the batch sputtering process technology. ITRT attempts to combine the technology and the industrial engineering knowledge to optimize manufacturing process parameter settings.
For the extremely complicated solar energy selective absorption film continuous sputtering process, plenty of parameters would affect the output. If we only rely on engineer’s experience to determine the values, the defect rate may increase owing to the unstable manufacturing process. This study proposes a systematic procedure for parameter optimization of solar energy selective absorption film continuous sputtering process. First, historical data and engineering knowledge are used to determine the significant factors. Second, Taguchi methods are employed to find the optimal combination of parameters. Finally, back-propagation neural network (BPN), desirability function, and genetic algorithms (GAs) are utilized to obtain the optimal parameter level combination.
The experimental results present that the proposed method can control the core of the problem efficiently. By simply employing 18 data and adjusting 7 factors, this research can enhance performance metrics to an international high quality standard. Our proposed method can restrain 27,553 tons of carbon dioxide every year with respect to beneficial energy conservation, which is 11 times less emissions than what the traditional paint process produces. From the perspective of industry, this result is considerably impressive.

目錄 V
圖目錄 VII
表目錄 VIII
第一章　緒論 1
1.1　研究背景與動機 1
1.2　研究目的 7
1.3　研究方法與流程 7
1.4　論文架構 9
第二章　太陽能選擇性吸收膜連續濺鍍製程與原理介紹 10
2.1　太陽能選擇性吸收膜發展歷史 10
2.2　真空磁控濺鍍技術 11
2.3　光學膜簡介 13
2.4　真空濺鍍於太陽能光學膜之生產應用 15
2.5　捲對捲連續式真空濺鍍製程影響因子 20
第三章　相關研究 21
3.1　田口方法 21
3.1.1　田口方法之基本原理 21
3.1.2　品質損失函數 25
3.1.3　直交表 27
3.1.4　信號雜音比 32
3.1.5　確認實驗 36
3.1.6　多重回應值問題的田口方法最佳化 38
3.2　類神經網路 39
3.2.1　類神經網路之基本架構 39
3.2.2　類神經網路之類型 42
3.2.3　倒傳遞類神經網路 44
3.3　期望函數 47
3.4　基因演算法 48
3.4.1　基因演算法之基本原理 48
3.4.2　基因演算法步驟 49
3.4.3　基因演算法的優點 55
3.5　製程參數最佳化之應用 55
第四章　研究方法 58
第五章　個案探討 61
5.1　尋找回應值與關鍵製程參數 61
5.2　應用田口方法求取最佳參數水準組合 63
5.3　應用本研究方法求取最佳參數水準組合 72
5.4　效益分析 79
第六章　結論 81
6.1　實驗結論 81
6.2　實驗結果分析 81
6.3　個案研究之貢獻 82
6.4　未來展望 83
參考文獻 85
附錄A　同時最佳化多個回應值 90
附錄B　NeuralWorks對吸收率α各參數的轉換尺度化公式 96
附錄C　NeuralWorks對放射率ε各參數的轉換尺度化公式 97
附錄D　建立之類神經網路運算模式 98
附錄E　10組基因演算組合個別確認實驗 101

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