博碩士論文 964306009 詳細資訊




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姓名 張筱芬(Hsiao-Fen Chang)  查詢紙本館藏   畢業系所 工業管理研究所在職專班
論文名稱 以決策樹法歸納關鍵製程暨以群集法識別關鍵路徑
(Critical Processes Induction with CART And Critical Path Identification with Clustering)
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摘要(中) 許多的品質工具以及手法被發展以及運用於各產業。這些工具以及手法協助組織確認出關鍵品質特性或是關鍵製程。這些方法也被廣泛運用於液晶顯示器產業。其中最為普遍運用的品質工具之一為失效模式分析,這項工具幫助組織確認出關鍵品質特性或是關鍵製程。此方法為藉由風險優先數的計算定義出關鍵品質特性或是關鍵製程,若此風險優先數為最大或是大於某特定數, 該項特性則視為關鍵品質特性或是關鍵製程。雖然對於RPN 之組成要素嚴重度(S)、發生度(O)、和難檢度(D) 有評分準則, 但此方法中評分結果仍被視為主觀的。
本研究主要為提議以資料探勘之方法論並自一本國面板廠商搜集相關資料。企圖以客戶聲音為目標, 將客戶聲音轉換成關鍵品質。我們運用了其中一項決策樹之方法, 分類及迴歸樹規納出影響每個批次產品品位之製程。進一步地,將導出之關鍵製程與每批次品位數以群集法分群並定一指標,藉由以此方法,可發現每一製程表現較好與較差的機台,關鍵製程中表現較好的機台便構成了關鍵路徑。
摘要(英) Many quality tools and techniques have been developed and deployed extensively in the industry-wide to help the organization to identify and determine the significant characteristic of product and critical process in the organization. Those quality tools are adopted in the TFT-LCD industry too. One of the most commonly used techniques is the failure mode and effect analysis (FMEA) that helps to identify and determine the significant characteristic of products and critical process in the organizations. The key characteristic and critical process is determined if its RPN is the biggest or bigger than a specific one. Although there are guidelines for the scoring, the outcome is relatively subjective. Although there are guidelines for the scoring, the outcome is relatively subjectively.
This research is proposing another data mining procedure and collecting the dataset from a local LCD manufacturer. We attempt to aim at the Voice Of Customer (VOC), i.e. what customer requirements to our product are and transfer the VOC to critical to quality. The one of the decision tree techniques, Classification and Regression tree (CART) will be adopted. . With CART modeling, the decision tree inducts the processes that affect the total quantity of product rank per lot. Further, product rank are grouped and analyzed by the clustering model. Also we define an index to decide the equipment performance. Based on the result, the superior and the inferior equipments of each process are identified. The critical path is composed of the superior equipments. Meanwhile, the result can help the management to analyze the variation between equipments.
關鍵字(中) ★ 資料探勘
★ 決策樹
★ 群集法
★ 液晶顯示器
關鍵字(英) ★ data mining
★ decision tree
★ CART
★ Clustering
★ LCD
論文目次 Chinese Abstract I
English Abstract II
Acknowledgement III
Content IV
The Content of Figure VI
The Content of Table VII
1. Introduction 1
1.1 Background 1
1.2 Motivation 2
1.3 LCD Process 4
1.3.1. Array Process 4
1.3.2. CELL Process 6
1.3.3. Module Process 7
2. Literature Review 8
2.1 Failure Mode and Effect Analysis ( FMEA) 8
2.1.1. The objective of failure mode and effect analysis (FMEA) 8
2.1.2. Product, Process and Service characteristics 9
2.1.3. The Process of conducting an FMEA 11
2.1.4. The Challenge of RPN 13
2.2 Data Mining Technique 15
2.2.1. Data Mining introduction in brief 15
2.2.2. Basic Data mining tasks 16
3. Methodology 18
3.1 Classification and Regression Tree 18
3.1.1. Overview of CART 18
3.1.2. Splitting rule 19
3.1.3. Pruning 21
3.1.4. Researches of decision tree application in the manufacturing industry 22
3.2 K-means Clustering 23
3.2.1. Overview of K-means 23
3.2.2. K-means Algorithm 24
3.2.3. Researches with regard to Clustering 25
4. Critical Process induced by Data mining 26
4.1 Business understanding 26
4.2 Data Understanding 26
4.3 Data Preparation 27
4.4 Modeling and Evaluation 29
4.4.1. Critical Process determined by CART 29
4.4.2. Identify the Critical Production Path by Clustering 35
5. Summary 39
5.1 The Research Summary 39
5.2 Future Research 39
Reference 40
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指導教授 曾富祥(Fu-Shiang Tseng) 審核日期 2009-5-23
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