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研究生:黃彥淳
研究生(外文):Yen-Chun Huang
論文名稱:時間稽延及前饋類神經網路的整合系統應用於印刷電路板製程孔破的預測
論文名稱(外文):A Hybrid System Combined by Time-delay and Feedforward Neural Networks to Predict Wall Hole in Manufacturing Process of Printed Circuit Board
指導教授:謝建興
指導教授(外文):Jiann-Shing Shieh
口試委員:徐業良江行全
口試委員(外文):Yeh-liang ShiuShing-Quan Jiang
口試日期:2016-07-04
學位類別:碩士
校院名稱:元智大學
系所名稱:機械工程學系
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:2016
畢業學年度:104
語文別:英文
論文頁數:39
中文關鍵詞:印刷電路板通孔化銅製程時間稽延類神經網路前饋類神經網路
外文關鍵詞:printed circuit board (PCB)plating through the hole (PTH)time-delay neural network (TDNN)feedforward neural network (FFNN)
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此項研究起源於印刷電路板製程中的缺陷。藉由先豐通訊公司提供的460天的數據資料,我們能藉此探討化銅製程中所須的各項藥液濃度異常和變化量異常與孔破發生的關係,而能建構出一個時間稽延及前饋類神經網路的整合系統用以預測孔破的發生的孔破預警系統。為了使預測最佳化,文中使用相關係數和均方根誤差來決定神經元和隱藏層的數目。在最佳化後,時間稽延類神經網路的預測結果和真實輸出的相關係數從0.3 提升至0.63。而每項藥液數據在訓練前皆經由前處理變成異常濃度和異常變化量兩種因子;從中也能定制出更為精準的藥液控制範圍。從而希望能達成藉由控制濃度而預防孔破發生的目標。在離線測試中,此系統能預測未成三天內孔破發生的機率,在第一天的準確率為0.96。
This study is originated due to the defects happening in printed circuit board manufacturing process. The study investigated the relationship between concentration of chemical and wall holes when the concentration is especially abnormal from the average. In this study a wall hole forecasting system is developed by combining time-delay and feedforward neural networks. In order to optimize the system, the method to decide numbers of hidden layers and hidden nodes in neural network has been investigated via two parameters of correlation coefficient and root mean square error. After optimization, the correlation coefficient compared between result nonlinear mapped by neural network and real data raised from 0.3 to 0.63. The system enables to predict the possibility for the wall hole happening in the next 3 days. In offline testing, the accuracy between real and predicted result is 0.96 for day one.
Chapter I: Introduction
1.1 Literature Survey……………………………………………………………..............1
1.2 Printed circuit board manufacturing process………………………… 4
Chapter II: Methodology
2.1 Preprocessing….…………………………………………………………............…6
2.2 Artificial neural network. …………………………………………………...13
2.3 Time-delay neural network…………………………………………….…...….14
2.4 Neuron analysis of depth of anesthesia case………..…21
2.5 The architecture of feed-forward neural network….....22
2.6 A hybrid system combined of time-delay and a feed-forward ANN………….........................................23
Chapter III: Results
3.1 TDNN...……………………………………………………………………................25
3.2 FFNN…………………………………………………………………….................. 28
3.3 Hybrid system combined by TDNN and FFNN…………………………29
Chapter IV Summary and Future Work……………………………………………….....33
References…………………………………………………………………………...................35

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