臺灣博碩士論文加值系統

在統計製程管制中，當製程受到多個具有相關性之品質特性所影響時，稱為多變量品質管制問題。由於科技之進步，先進的自動化檢測設備能同時量測一件產品中多項品質特性，若同時以多個單變量管制程序監控此多項品質特性，將由於特性間的相關性而造成過大的判斷誤差。過去有許多學者應用統計原理提出多種不同的多變量製程管制程序，以偵測多變量製程平均值或變異性之變異情況，但其效益仍有改善空間。因此，多變量製程管制程序的發展與其效益之提升，已是刻不容緩。

本研究以類神經網路為基礎，發展一監控多變量製程平均值與變異性變化之系統，此系統並可進一步估計平均值偏移量的大小。本研究採用不同的網路輸入向量供類神經網路進行學習。在製程偏移之偵測與偏移量之估計上，以平均連串長度與絕對誤差百分比來評估類神經網路的偵測能力，並與傳統管制法進行比較。此外，本研究並以模擬數值範例驗證類神經網路管制法之成效。研究成果顯示，類神經網路的偵測效益優於傳統管制法。

本研究的主要貢獻有三點。第一、本研究發展之類神經網路管制法的效益，並不會因共變異矩陣 ( ) 不同而有所差異，只要多個變量之間所產生之偏移量 ( ) 相同，則具有相同的偵測效益。第二、傳統的多變量CUSUM管制法有極佳之偵測效益，但其必須在偏移量已知的情況下使用，才能達到最佳的偵測效益。而本研究所提出之類神經網路管制法，可監控較大範圍之製程偏移量，使其在製程偏移量未知之情況下，偵測效益優於傳統之多變量CUSUM管制法。第三、類神經網路管制法對於製程偏移量的估計優於傳統管制法。製程偏移量之估計，將有助於工程人員擬定製程調整之對策。

Quality-control problems in several related variables are called multivariate quality-control problems. Various types of quality control charts have been proposed to monitor the mean or variance of a multivariate process. Besides the use of control charts in monitoring process and identifying assignable causes, quality practitioners frequently need to adjust processes based on the magnitude of change.

In this research, we develop a control procedure based on artificial neural network to monitor the mean and variance of the multivariate process. At the same time, this procedure will predict the magnitudes of changes to provide the engineers to modify the process. The performance of the artificial neural network and traditional multivariate quality control method have been evaluated by the average run length (ARL) and mean absolute percent errors (MAPE) using simulation. The results indicate that artificial neural network has certain advantage over the traditional multivariate quality control method.

The feature of this research is that even if the magnitude of changes is unknown, the detecting ability of neural network is better than traditional multivariate CUSUM method.

摘要 i
英文摘要 iii
誌謝 iv
目錄 v
表目錄 vii
圖目錄 ix


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 3
1.4 評估標準 3
1.5 研究方法與步驟 4
1.6 論文架構 5

第二章 文獻探討 7

2.1 單變量統計製程管制法 7
2.2 多變量製程平均值管制法 9
2.3 多變量製程變異性管制法 16
2.4 類神經網路在偵測製程變異之應用 19
2.4.1 應用類神經網路偵測製程平均值之偏移 20
2.4.2 應用類神經網路偵測製程變異性之變動 21
2.4.3 應用類神經網路偵測製程平均值、變異性之偏移 23

第三章 研究方法 24

3.1 倒傳遞網路 24
3.2 偵測多變量製程變異之類神經網路 26
3.2.1 訓練樣本 27
3.2.2 類神經網路之架構 29
3.2.3 訓練樣本的參數組合與產生 31
3.2.4 類神經網路之訓練 34

第四章 類神經網路之效益評估 35

4.1 評估方法 35
4.2 偵測平均值效益之評估與比較 36
4.3 偵測變異性效益之評估與比較 54
4.4 利用反應曲面法最佳化參數設定 57
4.5 數值範例 64

第五章 結論 69

參考文獻 71

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