臺灣博碩士論文加值系統

量測誤差(GME)由於能夠更準確地反映出製程能力的評估與衡量，因此在許多生產型工業裡扮演著重要的腳色。近年來，大量探討量測誤差於製程能力評估的研究僅針對於對稱公差的情形。事實上，在現實的生產案例中也會面臨非對稱公差的製程能力評估。為此，本文透過廣義信賴區間法(GCI)、抽樣分配法(SD)以及修正型抽樣分配法(MSD)來分析在非對稱公差的條件下量測誤差於製程能力評估的影響。此外，本文透過一系列各種不同參數條件下的模擬分析來比較其方法的績效以做出進一步的討論。最後，透過比較涵蓋率(CR)與平均下信賴界限(AVLCB)，本文所提出的方法顯示其良好的適用性來應用在非對稱公差的條件下考慮量測誤差於製程能力評估。此外，本文最後亦透過一個非對稱的案例來說明如何在真實環境下應用所提出的方法來更精確的評估製程能力。

Gauge measurement errors (GME) began to play an important role in many types of manufacturing industries because it would affect the estimation and assessment of the process capability. In recent years, the issue of process capability assessment in the presence of GME for cases with symmetric tolerances was investigated enthusiastically. However, even processes with symmetric tolerances are very common in practical situations, cases of asymmetric tolerances also occur in manufacturing industries. In this dissertation, generalized confidence interval (GCI), sampling distribution (SD), and modified sampling distribution (MSD) approaches are applied to assess the performances of processes with asymmetric tolerances in the presence of the GME. To examine and compare the performance of the proposed approaches, an exhaustive simulation under various conditions was conducted. The conclusion is that the proposed approaches appear quite satisfactorily for assessing process performance with asymmetric tolerances in the presence of GME in terms of the Coverage Rate (CR) and the Average Value of Lower Confidence Bound (AVLCB). Examples are presented to illustrate the applicability of the proposed approaches in real factory condition.

TABLE OF CONTENTS
摘要 ii
Abstract iii
Acknowledgements iv
Table of Contents v
Table Of Symbols vii
Content of Figures viii
Content of Tables xviii
Chapter 1. Introduction 1
1.1. Research Background 1
1.2. Research Motivations 2
1.3. Research Objectives 3
1.4. Research Organization 3
Chapter 2. Literature Review 7
2.1. Process Capability Indices 7
2.1.1. Process Capability Indices for Symmetric Case 7
2.1.2. Process Capability Indices for Asymmetric Case 9
2.2. Gauge Measurement Errors 19
Chapter 3. Methodology 23
3.1. Generalized Confidence Interval (GCI) Approach with Gauge Measurement Errors 24
3.1.1. GCI approach with GME for Index 25
3.1.2. GCI for Index 27
3.2. Sampling Distribution (SD) and Modified Sampling Distribution (MDS) Approaches 31
3.2.1. (SD/MSD) for Index in the presence of GME 31
3.2.2. (SD/ MSD) for Index in the presence of GME 34
Chapter 4. Performance Comparisons 40
4.1. Simulation Layout Setting 40
4.2. Simulation Results Analysis for Index 49
4.2.1. Coverage Rate (CR) Analysis 49
4.2.2. Average Value of Lower Confidence Bound (AVLCB) Analysis 62
4.3. Simulation Results Analysis of Index 71
4.3.1. Coverage Rate (CR) Analysis 72
4.3.2. Average Value of Lower Confidence Bound (AVLCB) Analysis 83
Chapter 5. Application Examples 94
Chapter 6. Conclusions and Future Works 102
References 105
Appendix 109
Biography 232

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