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中文論文名稱 利用製程能力指標Cpm建立品質特性分析圖並用之改善產品品質
英文論文名稱 Using process capability index Cpm to construct a quality characteristic analysis chart for product quality improvement
校院名稱 淡江大學
系所名稱(中) 管理科學學系博士班
系所名稱(英) Doctoral Program, Department of Management Sciences
學年度 103
學期 1
出版年 104
研究生中文姓名 楊俊明
研究生英文姓名 Chun-Ming Yang
學號 899620149
學位類別 博士
語文別 中文
口試日期 2014-12-07
論文頁數 76頁
口試委員 指導教授-歐陽良裕
指導教授-陳坤盛
委員-徐世輝
委員-吳錦全
委員-王孔政
委員-林谷鴻
委員-林碧川
委員-曹銳勤
中文關鍵字 製程能力指標  準確度  精確度  品質特性分析圖  熵法  理想解類似度順序偏好法  模糊理論 
英文關鍵字 Process capability index  accuracy  precision  QCAC  entropy  TOPSIS  fuzzy theory 
學科別分類
中文摘要 製程能力指標是一種有效且方便用以衡量產品品質水準的方法,然而單一製程能力指標並無法明確地指出造成不合格品質特性的真正原因。另一方面,雖然多品質特性分析圖已被廣泛地用以衡量產品具多個不同型式品質特性的品質水準。但由於多品質特性分析圖是由多個製程能力指標所構成,可能會增加品質管理者在計算上的複雜度和錯誤率,並且它也無法辨識望大型或望小型不合格品質特性是由於製程偏移或/和變異過大所造成。此外,對於企業而言,它可能會同時考量改善不合格品質特性所需耗用的資源以及改善後可獲得的效益,和決策者主觀判斷上具有不確定性的情況。是故,如何運用一套有效的方法去衡量具單一品質特性或多個不同型式品質特性的產品品質水準,並分析造成不合格品質特性的原因及決定何者為被優先考慮改善的項目是非常重要的課題。
有鑒於此,本論文將上述研究問題分為五個章節進行探討。第一章首先說明本論文之研究動機與目的、相關文獻探討和論文架構。第二章將製程能力指標Cpm、最小個別品質能力指標、準確度及精確度做結合,建立一個品質特性分析圖。藉由資料在圖形上的分布情況,便可找出不合格品質特性並判斷不合格品質特性產生缺失的原因是由於製程偏移或/和變異過大所造成。接著,計算各不合格品質特性的判別距離。當品質管理者考量總改善成本有限制的情況下,可根據判別距離值的大小進行排列不合格品質特性改善的先後順序。第三章延續第二章,將六標準差概念加入品質特性分析圖,建構一個新的品質特性分析圖並將之與熵法和理想解類似度順序偏好法做結合。品質管理者便可在考量改善每個不合格品質特性所需耗用的資源以及改善後可獲得的效益不同情況下,決定不合格品質特性改善的先後順序。第四章利用變數變換法將望目型、望小型和望大型的品質特性之樣本觀測資料做適當變化,進而建立一個多品質特性分析圖並將之與模糊理想解類似度順序偏好法做結合。品質管理者可衡量望目型、望小型和望大型的品質特性是否合格,並在考量改善每個不合格品質特性所需耗用的資源以及改善後可獲得的效益不同,且為語意變數的情況下,進行排列不合格品質特性的改善先後順序。最後,第五章則對本論文各章所提出的方法和研究結果做一總結,同時建議後續研究者未來可進行的研究方向。
英文摘要 Process capability indices (PCIs) are an effective and easy–to–use approach for measuring the quality level of products in the manufacturing industry. However, using a single PCI cannot effectively reveal the causes of deficient quality characteristics in the manufacturing process. On the other hand, some multi–quality characteristic analysis charts (MQCACs) combining two or more PCIs have been proposed to overcome the problem products with two or more different types of quality characteristics. However, if quality managers use these existing MQCACs to evaluate whether the quality capability of a product meets the acceptance standard, the numerical analysis and calculation of the PCIs will be quite complex, which will directly affect the outcome of the analysis and entail a greater expenditure of time, cost and manpower. Also, these existing MQCACs cannot be useful in identifying problems with all substandard quality characteristics in larger–the–better and smaller–the–better situations due to process shift and/or variability. In practice, manufacturers should consider how to prioritize improvements that need to be made in all substandard quality characteristics in light of resource requirements, the potential for performance improvements and the uncertainty represented in decision data. Therefore, a key issue faced by the manufacturing industry is determining how to measure single or multiple quality characteristics and prioritize improvements to be made to all substandard quality characteristics of a product with respect to resource requirements and performance improvement potential.
This dissertation is organized into five chapters in order to analyze and discuss the abovementioned issues. Chapter one covers the motivation, objectives, framework of this dissertation, and a literature review on PCIs. Chapter 2 illustrates how to combine the process capability index Cpm, minimum individual quality capability index C0, accuracy A and precision P to construct a quality capability analysis chart (QCAC). From the scatter diagram of related data on the QCAC, we can find all of the substandard quality characteristics, and identify the causes of all the substandard quality characteristics in a product owing to process shift and/or variability. Meanwhile, the values of the discrimination distance (DD) of all the substandard quality characteristics are calculated. Quality managers can use the values of the DD to rank all the substandard quality characteristics slated for improvement in order of priority if the total budget for all the substandard quality characteristics improvements is limited. Chapter 3 adds to the QCAC in Chapter 2 with the concept of six sigma to construct a new QCAC. Next, the new QCAC is tailored to combine entropy and technique for order preference by similarity to ideal solution (TOPSIS) methods into a QCAC–Entropy–TOPSIS approach. Quality managers can categorically prioritize improvement options for all substandard quality characteristics with respect to resource requirements, and consider the potential for performance improvements. Chapter 4 first uses the change–of–variable technique to transfer all of the sample data of nominal–the–best, larger–the–better and smaller–the–better quality characteristics into new evaluation data. Meanwhile, an MQCAC can be established as a powerful tool for finding all substandard quality characteristics of a product. Next, a novel hybrid method is presented that integrates a new MQCAC and a fuzzy technique for order preference by similarity to ideal solution (fuzzy TOPSIS). Quality managers can clearly prioritize improvement options for all substandard nominal–the–best, larger–the–better and smaller–the–better quality characteristics with respect to resource requirements and performance improvement potential under a fuzzy environment. Finally, Chapter 5 provides a summary of the main findings and conclusions of this dissertation, and offers suggestions for the direction of future work.
論文目次 目次
目錄 II
表目錄 IV
圖目錄 VI
通用符號一覽表 VII
目錄
第1章 緒論 1
1.1 研究動機與目的 1
1.2 相關文獻探討 4
1.2.1 製程能力指標 4
1.2.2 製程能力指標應用於多品質特性之發展 7
1.3 本文架構 10
第2章 利用品質特性分析圖找出產品不合格品質特性之改善順序 13
2.1 品質特性分析圖 13
2.2 利用品質特性分析圖找出產品不合格品質特性之改善順序的實施步驟 17
2.3 應用實例 19
2.4 小結 23
第3章 結合品質特性分析圖、熵法與理想解類似度順序偏好法找出產品不合格品質特性之改善順序 24
3.1 六標準差下品質特性分析圖 24
3.2 熵法 26
3.3 理想解類似度順序偏好法 28
3.4 結合品質特性分析圖、熵法與理想解類似度順序偏好法找出產品不合格品質特性之改善順序的實施步驟 29
3.5 應用實例 31
3.6 小結 36
第4章 結合多品質特性分析圖和模糊理想解類似度順序偏好法找出產品不合格品質特性之改善順序 38
4.1 六標準差下多品質特性分析圖 38
4.2 模糊理論 42
4.3 結合多品質特性分析圖和模糊理想解類似度順序偏好法找出產品不合格品質特性之改善順序的實施步驟 45
4.4 應用實例 49
4.5 小結 61
第5章 結論與建議 62
5.1 主要研究成果 62
5.2 後續研究建議 64
參考文獻 66
附錄 76
表目錄
表1.1 MQCAC的發展 9
表2.1 不同品質水準與c值的對應關係 14
表2.2 H型號晶片電阻器之品質特性 21
表2.3 H型號晶片電阻器各品質特性之準確度估計值Ai、精確度估計值Pi與Cpmi值 22
表3.1 不同品質水準和t值下的C0值 24
表3.2 Y型號花鼓快拆的品質特性及其相關數值 32
表3.3 Y型號花鼓快拆各品質特性之準確度估計值Ai、精確度估計值Pi與Cpmi值 33
表4.1 評估各項評估準則重要性之語意尺度及其所對應的三角模糊數 45
表4.2 評估各備選方案在各項準則下所需耗用資源或改善後可獲得效益之語意尺度及其所對應的三角模糊數 45
表4.3 95無鉛汽油各品質特性的規格要求 50
表4.4 95無鉛汽油各品質特性之之平均數估計值μyi、標準差估計值σyi與Cpmi值 52
表4.5 3位決策者對各項評估準則重要性所給予的語意尺度 53
表4.6 3位決策者對各備選方案在各項評估準則下改善所需耗用的資源或改善後可獲得的效益所給予的語意尺度 53
表4.7 3位決策者對各項評估準則重要性的三角模糊數 54
表4.8 3位決策者對各備選方案在各項評估準則下改善所需耗用的資源或改善後可獲得的效益的三角模糊數 54
表4.9 全體決策者對各項評估準則重要性的聚合三角模糊數 55
表4.10 全體決策者對各備選方案在各項評估準則下改善所需耗用的資源或改善後可獲得的效益的聚合三角模糊數 56
表4.11 各備選方案在各評估準則下之標準化模糊數 57
表4.12 各備選方案在各評估準則下之加權標準化模糊數 58
表4.13 模糊正理想解向量V+和模糊負理想解向量V- 59
表4.14 各備選方案的正、負分離度、相對近似度、標準化的判別距離、加權的相對近似度及被選取的先後順序 60
圖目錄
圖1.1 三個不同製程之Cp、Cpk與Cpm值比較 6
圖1.2 本文架構 12
圖2.1 QCAC (當品質水準為"有能力(取c=1)"和t=5時) 15
圖2.2 點(Ai, Pi)的判別距離(DD)(當品質水準為"有能力"和t=5時) 16
圖2.3 運用QCAC找出不合格品質特性並排列其改善的先後順序之流程圖 19
圖2.4 H型號晶片電阻器 20
圖2.5 H型號晶片電阻器的QCAC 21
圖3.1 3σ、4σ、5σ和6σ下的QCAC (當t=3時) 25
圖3.2 Y型號花鼓快拆 32
圖3.3 Y型號花鼓快拆的QCAC 33
圖4.1 3σ、4σ、5σ和6σ下的MQCAC (當t=9時) 40
圖4.2 點(μyi, σyi)的判別距離(DD)(當品質水準為4σ和t=9時) 41
圖4.3 三角模糊數A=(a, b, c)的隸屬函數圖形 43
圖4.4 95無鉛汽油的MQCAC 51
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