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研究生:鄭凱文
研究生(外文):Kai-WanCheng
論文名稱:應用類神經網路、田口方法、主成份分析法與順序偏好法於多重品質特性製程改善之研究
論文名稱(外文):Process Improvement with multiple quality characteristics via ANN, Taguchi, PCA and TOPSIS
指導教授:王泰裕王泰裕引用關係
指導教授(外文):Tai-Yue Wang
學位類別:碩士
校院名稱:國立成功大學
系所名稱:工業與資訊管理學系專班
學門:商業及管理學門
學類:其他商業及管理學類
論文種類:學術論文
論文出版年:2012
畢業學年度:100
語文別:中文
論文頁數:74
中文關鍵詞:田口方法多重品質特性製程最佳化主成份分析順序偏好法類神經網路
外文關鍵詞:Taguchi methodmultiple responsesPCATOPSISANN
相關次數:
  • 被引用被引用:17
  • 點閱點閱:633
  • 評分評分:
  • 下載下載:142
  • 收藏至我的研究室書目清單書目收藏:2
過去常以田口方法處理單應變數最佳化問題而取得良好結果。然而,實務上許多製程所涉及的並不是單一屬性的問題。因此學者開始研究以田口方法搭配工程判斷的方法處理多重品質特性問題。但工程判斷無疑會增加決策過程的不確定性,同時在決定綜合品質指標時如何決定權重也是一項難題。
為了進一步解決上述問題,決策者需要一個較科學且有效的決策模式以解決多重品質特性問題。在本研究中,我們提出一個有系統整合田口方法、主成份分析法、順序偏好法以及類神經網路的多重品質特性最佳化模式。使工程設計人員於處理多重品質特性之最佳化問題時,可參考此模式,找出符合設計需求的最佳因子水準組合。在這個模式中,可以主成份分析法得到一組最佳組合(A),以順序偏好法得到一組最佳組合(B)。兩種設計的結果可以重現實驗加以比較,由於實驗結果本身存在著誤差,故以類神經網路模型對兩種組合所得的品質特性預測值作為重現實驗比較時的參考。
為了說明本研究模式,在本文中以高速火燄噴塗為個案說明問題,並提供實驗結果給後續研究者作為參考。實驗結果顯示,以主成份分析法所得的(A)組合可得到較佳的(Y2)表面應度與(Y3)微硬度。而以順序偏好法所得的(B)組合可得到較佳的(Y1)堆積率。以類神經網路模式對各實驗組合所得的品質特性預測值排序,結果與各實驗組合的重現實驗實際值排序結果相符。顯示以類神經網路模型應用在多重品質特性預測,有良好的預測績效。
In the past, the Taguchi method is an efficient approach for optimizing a single quality response. In practice, however most process has more than one quality response of main interest. Therefore, the multiple response problems in the Taguchi based method with engineer’s judgement have gained a considerable research attention. But without doubt, the judgement of an engineer will increase uncertainty during the decision- making process. And these techniques are either troublesome for determine the proper weight for each response while develop a combined quality response.
In order to solve above problem, the decision-maker need a scientific and effective model to solve multiple response problem. In this research, we propose a systematic procedure via Taguchi, PCA, TOPSIS and ANN. According to this model, the engineer can find optimization parameter level combination. In this model, a PCA method will first to be considered to find an optimization design (A) and a TOPSIS method will be considered separately to find an optimization design (B). The design result can be compared by replicate experiments. Since errors exist in the experiments, the ANN model will be used for prediction of responses as reference.
In order to introduce this model, a case study of HVOF coating process will be presented in this research and the experiment result will be provided for future research. According to the replicate experiments, the PCA method can get better (Y2) surface hardness and (Y3) micro-hardness and the TOPSIS method can get better (Y1) application rate. The ranking order for each response by ANN predictions is the same as replicate experiments do. This reveal the ANN model have good performance on multiple response predictions.
摘要 I
Abstract II
目錄 IV
表目錄 VI
圖目錄 VII
第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 2
1.3 研究範圍與限制 3
1.4 研究方法及架構 4
第二章 文獻探討 6
2.1 田口方法 6
2.2 多重品質特性最佳化方法之相關文獻探討 15
2.3 主成份分析 22
2.4 順序偏好法 26
2.5 類神經網路 31
2.6 文獻小結 41
第三章 研究方法 42
3.1 多重品質特性最佳化演算模式之建構 42
3.2 以主成份分析法設計最佳化參數 44
3.3 以順序偏好法設計最佳化參數 45
3.4 品質特性預測 47
3.5 研究方法小結 48
第四章 個案分析 50
4.1 個案說明 50
4.2 主成份最佳化設計 52
4.3 順序偏好法最佳化設計 55
4.4 類神經網路模型預測 58
4.5 重現實驗結果 61
第五章 結論 63
5.1 研究結論 63
5.2 後續研究與建議 64
中文參考文獻 65
英文參考文獻 66

中文參考文獻
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