臺灣博碩士論文加值系統

印刷電路板產業可說是一個製程技術發展水準相當高的電子科技產業，在眾多同業相互競爭之下，開發新製程技術提升其產品品質是必然的發展趨勢。目前作法都是工程師憑藉其經驗來選取可能影響品質的控制因子，輔以實驗設計、實驗批驗證，來訂定製程參數值調整範圍，但製程控制因子過多，且彼此間具有相關性，故於選取重要關鍵因子時非常困難。而新製程資料蒐集又有資料量少的原因，於資料分析上會造成傳統統計方法及一般資料探勘方法會造成偏誤。故本研究與蘇星樺(2003)合作，利用資料視覺化方法較不局限於參數維度與筆數多寡的特性，找出製程控制因子間與品質特性的相關性，作為造成品質特性缺陷之重要關鍵因子的選取依據，此成功實務案例，記錄於蘇星樺(2003)中。本研究首先將此視覺化資料探勘過程以系統化架構整理，並提出新的視覺方法以及維度排序方法解決蘇星樺(2003)方法中資料視覺呈現時資料覆蓋與視覺特徵雜亂的問題。另外，本研究提出不同視覺特徵之相關性權重量化指標，強化蘇星樺(2003)對於視覺特徵之相關性上定義的不足之處，幫助工程師能擁有更多有關於控制因子間相關性探討的資訊。

For companies in a highly-competitive industry such as PCB manufacturing to remain competitive in the market a critical issue is to keep developing new manufacturing processes that have lower cost or higher quality. In an effort cooperated with Su (2003) we have successfully developed a visual data mining process to address an important problem in developing a new manufacturing process: finding the process controlling parameters that affect the quality features of the product. In this research, we outline Su’s visual data mining work using a framework architecture. Then we point out several problems in Su’s work and propose solutions to these problems. First, we discuss the data-overlapping problem in parallel coordinate graph and propose survey plot to overcome this problem. Secondly, we discuss problems with Su’s dimensional layout and propose new method to layout the dimensions in parallel coordinates. Thirdly, we propose different way to obtain a quantitative measure for the visual patterns shown in a parallel coordinates. We use the same data in Su’s work and compare the results to show the improvement.

中文摘要 i
英文摘要 ii
誌謝 iii
目錄 iv
表目錄 vi
圖目錄 vii
圖目錄 vii
第一章 緒論 1
1.1 研究背景及動機 1
1.2 研究目的 2
第二章 文獻探討 3
2.1 知識發現(Knowledge Discovery)與資料探勘(Data Mining) 3
2.2 工業界製程問題的資料探勘應用分析 5
2.3 資料視覺化 6
2.3.1 圓形切割圖(Circle segment) 7
2.3.2 平行座標圖(Parallel coordinate) 7
2.3.3 綜觀圖(Survey pot) 7
2.3.4 散佈圖矩陣(Scatter-plot matrix) 8
2.3.5 放射視覺圖(Radviz) 8
2.4 平行座標法於資料視覺化之應用 10
第三章 PCB增層法新製程資料視覺探勘 12
3.1 資料蒐集與整理 12
3.2 資料表T視覺探勘 13
3.2.1 平行座標法視覺呈現與特徵說明 13
3.2.2 平行座標維度排序 15
3.2.3 特徵呈現區域圈選與確認 16
3.3 資料表T2視覺探勘 17
3.3.1 資料表T2平行座標圖與特徵說明 17
3.3.2 資料分群 19
3.3.3 平行座標矩陣圖 22
3.3.4 特徵呈現區域圈選及量化 22
3.3.5 高相關群組因子之資料表T視覺呈現 25
第四章 視覺探勘架構圖及應用案例 28
4.1 PCB新製程視覺化資料探勘架構圖 28
4.2 實務案例說明 32
4.2.1資料蒐集與整理 32
4.2.2資料表T視覺探勘 34
4.2.3資料表T2視覺探勘 37
第五章 T表視覺呈現方法改善 45
5.1 平行座標圖資料覆蓋問題 45
5.2 以綜觀圖輔助平行座標法 45
5.3 維度排序問題改善 48
5.4 PCB新製程資料資料表T視覺探勘改善案例 50
5.4.1維度排序視覺呈現 50
5.4.2圈選特徵呈現區域 52
5.4.3特徵呈現區域放大圖 52
5.4.4挑選特徵呈現維度 53
5.4.5案例改善結果比較 55
第六章T2表相關性權重計算方法改進與比較 56
6.1 T2表視覺特徵相關性權重計算方法 56
6.2 計算分群相關性權重加總 57
6.3 相關性權重計算方法比較 60
6.4 PCB新製程資料T2表相關性權重計算方法改進案例 61
第七章 結論與建議 75
參考文獻 77

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