臺灣博碩士論文加值系統

目前L公司生產印刷電路板，其生產線對重要品質特性的管制，仍沿用傳統的 X ̅-R 管制圖。由於傳統管制圖適用於大量且連續式的生產，但因應市場趨勢與訂單需求，留在台灣公司內部只剩下少量多樣、新產品或者特殊技術要求較嚴格的產品，原本使用在現場的大量生產 X ̅-R管制圖，無法及時有效發現製程中的品質變異。再者L公司使用的不合格率管制圖(p-chart)是製造單位各站的每月總不合格率統計資料，未將每日(或每批)生產量與不良數量分開統計及分析，因此無法明顯呈現該站或該批的不良資訊。
本研究主要對L公司製程品質管理的改善，應用適合的Short-Run小批量生產管制圖，提早偵測出品質的變異，分析變異原因，提出因應對策，防止因此變異而產生大量不良。
本研究採用現場的V-Cut與NC-R二個製程，運用1994年Salti 及Statham根據以前學者所提出的資料轉換方法及標準化管制圖(SCCs)，並比較幾種小批量管制圖，找出適合L公司目前小批量產品的管制圖；另一方面重新檢討不合格率統計方式，將每月不合格率統計改成每日統計方式，運用電腦將不合格率資料整理及統計後，可呈現出每個製程站別是否有變異情況。
根據本研究實際案例及研究結果顯示，轉換後的管制圖可適用小批量且不同管制規格，將其轉換後可呈現出異常批，這個方式在傳統的管制圖是無法發現異常點，明顯達到預期的目標 – 建立適用管制圖，偵測出品質的變異，防止大量不良產生，並協助建立適當的品質管理制度，運用重要品質特性判斷流程圖，找出適當的管制圖。
運用適當的不合格率管制圖的統計方式，可以呈現各單位不良的資料，可再依將統計資料做分析，運用品保的七大手法方式，進行不良原因分析、討探與改善，提升產品良率。

Currently, L company adopts traditional X ̅-R control chart to manage the important feature in production line for the PCB product.
As the traditional control diagram is ideal for the large number and continuous production, to meet the market trend and demand. The company has small quantity and various types, new products or special skill products with severe requirements be produced in Taiwan.
The used X ̅-R control chart in production line cannot reveal the quality variance happened in processing in time. Furthermore, the fraction nonconforming control chart (p chart) used by L company is the statistical data of monthly defective rate of each production station, not separating the daily (or per lot) number produced for statistics or analysis. It cannot clearly show the defective info. of each station or each lot.
This research focuses on the process quality management improvement of L company, adopting the ideal short-run small quantity control chart to detect the quality variance in early phase and analyze the root cause of the variance, then to propose the countermeasure to prevent the variance from leading to numerous defects.
This research adopted 2 processes, V-Cut and NC-R, in production line for analysis, using the data transformation method and standardized control charts (SCCs) provided by Salti and Statham in 1994, and compared several types of small quantity control charts to find out the control chart that meets the demand of L company’s small quantity production. Besides, by reviewing the statistical method of the defective rate, to change the monthly statistics to daily output, and make use of computer to collate the statistic data, afterward it can reveal if there is any variance issues of each station.
Based on the real case and study result of this research, it shows the transformed control chart is applicable for the small quantity, various controlled spec.
After transformed the data, the abnormal lots will be revealed. It’s impossible to find out the variance in traditional control chart with this method. It achieves the expected goals obviously – to build the model of proper control chart, to detect the quality variance and prevent the numerous defects produced and benefits the proper quality management system builds, to adopt the important quality features judging flowchart to find out the proper control chart.
With the proper statistics method of control chart for fraction nonconforming, it can reveal the defective data of each unit. Based on those statistical data, to apply The Magnificent Seven tools to do the defective root cause analysis for probing into and improving the issues, and then raise the yield rate of products.

目錄
摘要 iv
ABSTRACT vi
表 目 錄 xi
圖 目 錄 xii
第一章 緒論 1
第一節 研究背景 1
第二節 研究動機 3
第三節 研究目的 4
第四節 研究流程 5
第二章 文獻探討 6
第一節 印刷電路板(PCB) 7
第二節 統計製程管制(Statistical Process Control，SPC) 18
第三節 短小製程(Short Run) 28
第四節 資料轉換模式 38
第三章 研究方法 43
第一節 研究樣本及範圍 43
第二節 管制圖的延伸應用(p chart) 45
第四章 資料分析結果 47
第一節 量測值運用資料轉換法及管制圖比較 47
第二節 製程管制的改善 57
第五章 結論與建議 65
第一節 結論 65
第二節 建議 66
參考文獻 67
附錄一 70
附錄二 71
附錄三 74

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