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研究生:陳文忠
研究生(外文):Wen-Chung Chen
論文名稱:印刷電路板之少量多樣生產製程管制-以L公司為例
論文名稱(外文):Short-Run Process Control By Printed Circuit Board – An Example On L Company
指導教授:葉惠忠葉惠忠引用關係
指導教授(外文):Hui-Chung Yeh
口試委員:蔡憲唐李來錫
口試委員(外文):Hsien-tang TsaiLai-Hsi Lee
口試日期:2014-06-06
學位類別:碩士
校院名稱:國立高雄應用科技大學
系所名稱:企業管理系碩士在職專班
學門:商業及管理學門
學類:企業管理學類
論文種類:學術論文
論文出版年:2014
畢業學年度:102
語文別:中文
論文頁數:77
中文關鍵詞:短製程印刷電路板平均值-全距管制圖標準化管制圖精密成型加工不合格率管制圖
外文關鍵詞:Short-RunPrinted Circuit BoardX ̅-R ChartStandardized Control Charts(SCCs)NC-Rp chart
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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
一、中文部份
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2.白賜清(2004)。品質管制之統計方法。三民書局,新北市。
3.台灣電路板協會(2013) PCB市場評析系列P3. N.T. Information Ltd台灣電路板協會,桃園市。
4.台灣電路板協會(2005) 。印刷電路板概論.養成篇,台灣電路板協會,桃園市。
5.李友錚、賀力行(2012)。品質管理整合性思維,前程文化事業出版社,新北市。
6.房克成、林清風(2005) 。管制圖與製程管制,三民書局,新北市。
7.徐世輝(2006)譯Montgomery。品質管理,高立圖書,新北市。
8.胡充佑(2001)。少量多樣製程之微量異常偏移管制探討-以印刷電路板底片自動化檢測為例。朝陽科技大學工業工程與管理系碩士論文。
9.陳姿璇(2013)。應用風險調整CUSUM管制圖監控醫療資料之研究。國立雲林科技大學工業工程與管理系碩士論文。
10.張政勛(2000)。少量多樣生產型態之製程能力求算之研究。國立雲林科技大學工業工程與管理系碩士論文。
11.劉怡秀(2006)。改良式區間管制圖應用在短製程之研究。朝陽科技大學工業工程與管理系碩士論文。
12.劉福裾(1994)。新速製程能力指標之研究。國立交通大學工業工程研究所碩士論文。
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二、英文部份
1. Al-Salti ,M. and Stathamr, A.(1994). A Review of the Literature on the Use of SPC in Batch Production. Quality and Reliability Engineering International, Vol.10, 49-61.
2. Bothe, R. D., (1988). SPC for Short Production Run. Quality, Vol.27(12), 58-59.
3. Bothe, Davis R., (1989). SPC for Short Production Runs. IEEE Conference, 1960-1963.
4. Casti, E.D., and Montgomery, C.D.(1994). Short-Run Statistical Process Control:Q-Chart Enhancements and Alternative Method. Quality and Reliability Engineering International, Vol.10, 87-97.
5. G. K. Griffith, (1996), Statistical Process Control Methods for Long and Short Runs, 2nd ed. Milwaukee: ASQC Quality Press, 1996, 250 pp.
6. Holmes, D.S., (1997). Process acceptance chart for short runs. Quality Engineering, Vol. 10(1), pp. 149-153.
7.Hillier, F.S., (1964). X ̅ chart control limits based on a small number of subgroups. Industrial Quality Control, Vol. 20(8) 24-29.
8.Hillier, F.S., (1969). X ̅ and R chart control limits based on a small number of subgroups. Journal of Quality Technology, Vol. 1, 17-26.
9.Lucas, J. M. and Saccucci, M. S. (1990). Exponentially Weighted Moving Average Control Schemes : Properties and Enhancements, Technometrics, Vol. 32, pp. 1-16.
10.Montgomery, D. C. (1997), Introduction to Statistical Quality Control, New York:John Wiley.
11.Montgomery, D. C. (2001), Introduction to Statistical Quality Control, Journal of Quality Technology, Vol. 33(4), 524-525.
12.Noskievicova, D.(2013), Capability analysis for Leagifle manufacturing processes. Carpathian Control Conference (ICCC), 2013 14th International, 262-266.
13.Page, E. S. (1954), Continuous inspection schemes. Biometrika 41, 100-115.
14.Quesenberry, C.P., (1991). SPC Q Charts for Start-Up Processes and Short or Long Runs. Journal of Quality Technology, Vol. 23(3), 213-224.
15.Quessenberry, C.P., (1991). SPC Q Chart for a Binomial Parameter p:short or long runs. Journal of Quality Technology, Vol. 23(3), 239-246.
16.Quesenberry, C.P., (2000). Statistical process control geometric Q-chart for nosocomial infection surveillance. American Journal of Infection Control, Vol. 28(4), 314-320.
17.Robert,S.W.(1959). Control Chart Tests Based on Geometric Moving Averages, Technometrics 1, 239-250.
18.Saravanan, A. & Nagarajan, P.(2013) Implementation of Quality Control Charts in Bottle Manufacturing Industry. International Journal of Engineering Science & Technology; Vol. 5 Issue 2, p335-340,
19.W. A. Shewhart,(1924). The Economic Control of Quality of Manufactured Product. New York, Van Nostrand, 1931. xiv+501 pp.
20.W. E. Deming, (1982). Quality,Productivity,and Competitive Position, MIT, vii, 373 p.
21.Zantek Paul F. (2006), Design of Cumulative Sum Schemes for Start-Up Processes and Short Runs. Journal of Quality Technology, Vol. 38, 365-375.
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