臺灣博碩士論文加值系統

過去的製程能力指標，如Cp、 Cpk、Cpm和Cpmk 已經普遍有效的運用於線上品管(On-line)。相對缺乏對產品設計及製程規劃等線外品管(Off-line)的研究；當使用製程能力指標於線外品管時，產品設計者通常會將製程平均值設定為製程目標值，並且期望最小化製程變異，使製程能力指標值極大化，雖然產品品質提升，卻忽略製程平均值的設定與製程變異的最小化會影響到品質損失、公差成本，所以製程能力指標應用於線外品管時要在品質損失與製造成本之間取得平衡。
本研究將磨耗觀念直接導入製程能力指標中，並結合品質損失的概念，發展出新的製程能力指標Cpmc(Average)，以最大化製程能力指標為目標，使用數學軟體Mathematica找出最佳解，最後使用敏感度分析驗證Cpmc(Average)進一步證明磨耗率W和品質損失係數K的增加對Cpmc(Average)會減小，故Cpmc(Average)能更切合實務之製造流程。

Conventional process capability indices (PCIs), such as Cp, Cpk and Cpm, have been used widely and successfully in most of today’s manufacturing sectors. These process capability indices, however, can only measure the process consequence resulting from various combinations of process mean and process variance on site by measuring the output without concern as to cost. Hence, a process capability index, Cpmc, is developed. However, during production process, the quality value of output may change as time passes due to process deterioration, leading to unexpected product failure that is usually costly in both time and money. As is known, changes in process means acquired quality loss and variability, while process tolerance has an effect on tolerance-related costs and quality loss. Because there exists a dependency among time, initial setting of process mean, process mean, and process tolerance, they have to be determined simultaneously.
Thus, in this study, the influence from process deterioration is built into Cpmc expression for a life cycle consideration. Consequently, a high quality but low cost process design can be achieved during the early stages of product design and process planning.

目 錄
中文摘要 -------------------------------------------------i
英文摘要 ------------------------------------------------ii
目錄 ---------------------------------------------------iii
圖目錄 ---------------------------------------------------v
表目錄 --------------------------------------------------vi
第一章 緒論---------------------------------------------1
1.1 研究背景-----------------------------------------1
1.2 研究動機與目的-----------------------------------1
1.3 論文架構-----------------------------------------2
第二章 理論基礎與相關文獻-----------------------------4
2.1 製程能力指標-------------------------------------4
2.1.1 單變量製程能力指標-------------------------------4
2.1.2 多變量製程能力指標-------------------------------6
2.2 公差成本-----------------------------------------8
2.3 品質損失-----------------------------------------9
2.3.1 品質損失函數-------------------------------------9
2.3.2 品值損失函數類型--------------------------------12
2.3.3 平均品質損失------------------------------------14
2.4 製程變異數的估計--------------------------------15
2.5 製程平均值偏移概念------------------------------15
第三章 研究方法----------------------------------------17
3.1 問題描述----------------------------------------17
3.2 製程能力指標Cpmc(Average)-----------------------19
3.3 研究架構---------------------------------------------19
3.4 使用軟體介紹與流程-----------------------------------21
第四章 範例說明與數據分析------------------------------22
4.1 製程能力指標Cpmc(Average)範例-------------------22
4.2 磨耗率W之敏感度分析-----------------------------24
4.3 品質損失函數K之敏感度分析-----------------------26
第五章 結論--------------------------------------------28
參考文獻-------------------------------------------------29
圖目錄
圖1.1 研究流程圖------------------------------------------3
圖2.1 公差成本函數圖--------------------------------------8
圖2.2 田口二次品質損失函數圖-----------------------------10
圖2.3 傳統品質損失函數圖---------------------------------11
圖2.4 日本及美國工廠電視機顏色密度之分布圖---------------12
圖2.5 望小型品質損失函數圖-------------------------------13
圖2.6 望大型品質損失函數圖-------------------------------13
圖2.7 不對稱型品質損失函數圖-----------------------------14
圖2.8 製程平均數偏移製程目標值圖-------------------------16
圖3.1 以時間為基礎之質量損失函數-------------------------18
圖3.2製程能力分析流程圖----------------------------------20
圖3.3 分析流程圖-----------------------------------------21
圖4.1 最佳值a0之Cpmc (Average)圖-------------------------23
圖4.2 最佳值t之Cpmc (Average)圖--------------------------23
圖4.3 最佳值Cpmc (Average)圖-----------------------------24
圖4.4 不同磨耗率W對 Q*的影響-----------------------------25
圖4.5不同品質損失係數K對 Q*的影響------------------------27
表目錄
表4.1磨耗率W敏感度分析-----------------------------------25
表4.2品質損失係數K敏感度分析-----------------------------26

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