中 文 摘 要
製程能力分析已廣泛用於製造生產業界，其目的為估計、監督進而降低工業生產製程的變異，並且提供供應商與顧客之間共通的產品品質標準。最常用的製程能力分析方法除了分析判讀直方圖的資訊外，主要就是應用製程能力指標。傳統上在計算製程能力指標時，多半是以常態性資料為對象，然而在實務上卻存在很多非常態性製程，尤其是精密製造工業。因此，在非常態性資料下，如何分辨出製程的非常態性本質，如何適當的詮釋與應用製程能力指標，更進而發展出一套適用的非常態製程能力評估模式，提供實際上的應用，實為製造業界一直在探討的課題之一。基於此，本論文的研究目的即在於發展出一套適用於望小品質特性的非常態製程能力評估模式，期能達到下列三個目的：(1)在非常態性資料下，能分辨出製程的非常態性本質；(2)在非常態性資料下，能提供製程能力指標適當的詮釋與定義；(3)在非常態性資料下，能提供適切的製程能力評估模式。本論文所提出的分析方法與步驟主要有三個功能模組：一為資料分群模組，在原始資料輸入後，先做必要的分群，以確保製程能力評估的正確性；二為分配族群歸類模組，針對經驗上較能代表該製程特性的機率分配族群做擬合度檢定，選取最能吻合的某一分配族群計算必要的百分位數，或建立製程不良率的上限；三為製程能力指標估算模組，以良率為基礎加權計算各分群的良率、估計整體製程良率及估算整體製程的當量製程能力指標 。本研究利用此架構所發展出來的方法，在經過望小品質特性資料的實際測試驗證後，證實可以達到預期之目標。

關鍵字：製程能力分析、非常態製程、望小品質特性、近似分配函數
當量製程能力指標 Cpu

ABSTRACT
Process capability analysis has been widely used and applied in industrial production processes. The objective of a process capability analysis is to estimate, monitor, and furthermore may reduce variability in industrial production processes. Additionally, process capability analysis provides a common standard of product quality for suppliers and customers. The most popular way to assess process capability is to use histograms and process capability indices (PCIs). In practice, there are many non-normal processes existed in industrial production processes, especially in the precision industrial processes, so that the use of PCIs based on the normality assumption can be misleading in many cases. In this research, we propose a new model to evaluate the non-normal process capability with smaller-the-better quality characteristic in order to achieve three goals: (1) to reflect the process status more realistically and to identify the quality characteristics of the non-normal process correctly, (2) to define, interpret and use PCIs adequately, and (3) to evaluate the non-normal process capability properly. This new model includes three functional modules. The data collection and clustering module collects and analyzes the data of process quality characteristics to find its own data types, and then separate the data appropriately into some data segments. The distribution-fitted module treats each data segment by goodness of fit method to find their approximation distribution function, and then calculates the process control limits, percentiles and the upper bound of defective rate. Finally, the PCIs evaluating module defines and calculates individual PCI and aggregate PCI of the process ( particularly for Cpu and “Equivalent”Cpu ) based on yield information. After experimenting with some examples for processes of smaller-the-better quality characteristic, It shows that this approach could avoid besetments and mistakes which may be exists when using traditional methods.

Keywords: Process Capability Analysis, Non-normal processes,
Smaller-the-Better Quality, Approximation Distribution Function,
“Equivalent”Cpu

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