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研究生： 簡子偉
Tzu-wei Chien
論文名稱： 應用POBREP於量測重複性與再現性研究
Applying POBREP to a Gauge Repeatability and Reproducibility
指導教授： 王福琨
Fu-Kwun Wang
口試委員: 郭瑞祥
Ruey-Shan Andy Guo
羅士哲
Shih-Che Lo
學位類別： 碩士
Master
系所名稱： 管理學院 - 工業管理系
Department of Industrial Management
論文出版年： 2006
畢業學年度： 94
語文別： 中文
論文頁數： 122
中文關鍵詞： 量測重複性與再現性 、主成份分析 、POBREP
外文關鍵詞： Process-Oriented Basis Representations, Repeatability and Reproducibility, Principal Components Analysis
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  •  上一筆

    • 本研究利用POBREP(Process-Oriented Basis Representations)法，來處理多變量量測資料的重複性與再現性的評估。此方法能讓量測資料具有製程圖樣的意義，來幫助瞭解造成量測異常的原因。
    研究利用兩個模擬範例與一個實際案例來進行POBREP與PCA(Principal Component Analysis)方法的比較分析。結果顯示主成份分析法能提供量測系統為量具或評價方法是否異常，但是所造成異常的製程原因則是無法知道；然而POBREP法可診斷製程圖樣的變異，此變異可能說明造成量測系統異常的潛在原因。因此，研究可以得到POBREP法在解釋多變量製程與量測變異方面優於主成份分析法。

    This study investigated the POBREP (Process-Oriented Basis Representations) method to handle the repeatability and reproducibility estimation of a multivariate measurement data. POBREP can provide the meaningful pattern for multivariate measure data that help us understanding the causality of measurement data abnormally.
    Furthermore, we compared POBREP method with PCA (Principal Components Analysis) method using two simulation examples and one real case study. The result shows that PCA can only offer whether the measurement system is acceptable or not, but it can not provide gauge is unable to know which reasons caused it. Fortunately, POBREP can point out the abnormality of pattern and the variation of manufacturing processes. Therefore, we can conclude that POBREP method with multivariate data is better than PCA in explaining manufacturing process and measurement variation.

    摘要 i Abstract ii 目錄 iii 圖目錄 v 表目錄 vi 第一章 緒論 1 1.1 研究動機 1 1.2 研究背景 1 1.3 研究目的 2 1.4 研究範圍與限制 2 1.5 研究架構 3 第二章 文獻探討 5 2.1 量測重複性與再現性介紹 5 2.1.1 美國汽車工業協會(1998)之QS 9000量測系統分析手冊 5 2.1.2 何謂量測重複性與再現性(GR&R or GRR) 5 2.1.3 計量型量具研究的方法 7 2.1.4 量測重複性與再現性衡量指標 8 2.1.5 量測重複性與再現性研究的分析 8 2.2 量測重複性與再現性相關文獻回顧 9 2.3 POBREP相關文獻回顧 15 2.3.1 POBREP介紹 15 2.3.2 POBREP應用 20 2.3.3 主成份分析法(PCA)理論基礎 26 2.3.4 POBREP與PCA的比較 30 第三章 POBREP法於量測重複性與再現性之方法論 32 3.1 POBREP法於量測重複性與再現性之研究架構 32 3.2 前置作業階段 34 3.3 檢定多變量常態分配階段 37 3.4 應用POBREP法階段 40 3.5 應用主成份分析(PCA)法階段 43 3.6 變異數分析(ANOVA)階段 46 3.7 評估與分析階段 59 第四章 POBREP法於量測重複性與再現性之案例研究 69 4.1 Example 69 4.1.1 Exampe1--採主成份分析(PCA)之量測重複性與再現性研究 69 4.1.2 Example1--採獨立效應POBREP之量測重複性與再現性研究 74 4.1.3 Exampe2--採主成份分析(PCA)之量測重複性與再現性研究 80 4.1.4 Example2--採獨立效應POBREP之量測重複性與再現性研究 83 4.2 實際案例(Case) 88 4.2.1 實際案例--採單變量方法之量測重複性與再現性研究 88 4.2.2實際案例--採主成份分析(PCA)之量測重複性與再現性研究 95 4.2.3 實際案例--採獨立效應POBREP之量測重複性與再現性研究 100 4.3 章結論 105 第五章 結論與建議 109 5.1 結論與本研究之貢獻 109 5.2 研究建議與未來研究方向 110 參考文獻 111 附錄A- Example1模擬量測數據紀錄表 114 附錄B- Example2模擬量測數據紀錄表 118 附錄C-Fortran程式之多變量檢定步驟 121 附錄D-Fortran程式之POBREP轉換步驟 122

    1. 楊浩二(1982)，“多變量常態性檢定及應用之研究”，華泰書局。
    2. AIAG Editing Group, (1998). Measurement Systems Analysis, Automotive Industry Action Group.
    3. Apley, D. W. and Shi, J. (2001). “A Factor-Analysis Method for Diagnosing Variability in Multivariate Manufacturing Processes”. Technometrics, Vol. 43, No. 1, pp. 84-95.
    4. Barton, R. R. and Gonzalez-Barreto, D. R. (1996). “Process-Oriented Basis Representations for Multivariate Process Diagnostics”. Quality Engineering, Vol. 9, No. 1, pp. 107-118
    5. Burdick, R. K. and Borror, C. M. and Montgomery, D. C. (2003). “A Review of Methods for Measurement Systems Capability Analysis”. Journal of Quality Technology, Vol. 35, No. 2, pp. 342-354.
    6. Burdick, R. K. and Larsen, G. A. (1997). “Confidence Intervals on Measures of Variability in R&R Studies”. Journal of Quality Technology, Vol. 29, No. 3, pp. 261-273.
    7. Camelio, J. A. and Hu, S. J. (2004). “Multiple Fault Diagnosis for Sheet Metal Fixtures Using Designated Component Analysis”. Journal of Manufacturing Science and Engineering, Vol. 126, No. 1, pp. 91-97.
    8. Colón, H. I. and González-Barreto, D. R. (1997). “Component Registration Diagnosis for Printed Circuit Boards Using Process-Oriented Basis Elements”. Computers and Industrial Engineering, Vol. 33, No. 1, pp. 389-392.
    9. Ding, Y. and Ceglarek, D. and Shi, J. (2002). “Fault Diagnosis of Multistage Manufacturing Processes by Using State Space Approach”. Journal of Manufacturing Science and Engineering, Vol. 124, No. 2, pp. 313-322.
    10. Ding, Y., Zhou, S. and Chen, Y. (2005). “A Comparison of Process Variation Estimators for In-Process Dimensional Measurements and Control”. Journal of Dynamic Systems, Measurement, and Control, Vol. 127, No. 1, pp. 69-79.
    11. Dolezal, K. K. and Burdick, R. K. and Birch, N. J. (1998). “Analysis of a Two-Factor R&R Study with Fixed Operators”. Journal of Quality Technology, Vol. 30, No. 2, pp. 163-170.
    12. Foster, E. J. and Barton, R. R. and Gautam, N. and Truss, L. T. and Tew, J. D. (2005). “The Process-Oriented Multivariate Capability Index”. International Journal of Production Research, Vol. 43, No. 10, pp. 2135-2148.
    13. Fowlkes, W. Y. and Creveling, C. M. (1995). Engineering Methods for Robust Product Design. Addison-Wesley Publishing Co.
    14. Hotelling, H. (1933). “Analysis of a Complex of Statistical Variables into Principal Components”. Journal of Educational Psychology, Vol. 24, pp. 417-441, pp. 498-520.
    15. Johnson, R. A. and Wichern, D. W. (2002), Applied Multivariate Statistical Analysis, 5th Ed, Prentice Hall, pp80-81, pp. 426-461.
    16. Kowalski, C. J. (1973). “Non-Normal Bivariate Distributions with Normal Marginals”. American Statistician, Vol. 27, No. 3, pp. 103-106
    17. Liu, Y. G. and Hu, S. J. (2003). “Assembly Fixture Fault Diagnosis Using Designated Component Analysis”. Journal of Manufacturing Science and Engineering, Vol. 127, No. 2, pp. 358-368.
    18. Liu, Y. G. and Hu, S. J. (2005). “Assembly Fixture Fault Diagnosis Using Designated Component Analysis”. Journal of Manufacturing Science and Engineering, Vol. 127, No. 2, pp. 358-368.
    19. Mader, D. P. and Prins, J. and Lampe, R. E. (1999). “The Economic Impact of Measurement Error”. Quality Engineering, Vol. 11, No. 4, pp563-574.
    20. Mandel, J. (1972). “Repeatability and Reproducibility”. Journal of Quality Technology, Vol. 4, No. 2, pp. 74-85.
    21. Mardia, K. V. (1980). Tests for Univariate and Multivariate Normality. In: P. R. Krishnaiah ed., Handbook of Statistics. North-Holland Publishing Co., pp. 279-320.
    22. Montgomery, D. C. (2001). Design and Analysis of Experiments, 5th Ed., John Wiley & Sons, New York, pp. 66-69, pp. 531-534.
    23. Montgomery, D. C. and Runger, G. C. (1993a). “Gauge Capability Analysis and Designed Experiments. Part I: Basic Methods”. Quality Engineering, Vol. 6, No. 1, pp. 115-135.
    24. Montgomery, D. C. and Runger, G. C. (1993b). “Gauge Capability Analysis and Designed Experiments. Part II: Experimental Design Models and Variance Component Estimation”. Quality Engineering, Vol. 6, No. 2, pp. 289-305.
    25. Noaker, P. M. (1994). “When a Good Process Goes Bad”. Manufacturing Engineering, Vol. 112, No. 4, pp. 65-70.
    26. Padilla, V. O. (2005). Process Oriented Basis Estimation in Presence of Non-Orthogonal Basis Elements. Master of Sciences in Industrial Engineering.
    27. Pearson, K. (1901). “On Lines and Planes of Closest Fit to Systems of Points in Spaces”. Philosophical Magazine, Vol. 2, No. 6, pp. 559-572.
    28. Stout, G. (1994). “Measurement Put to The Test”. Quality, Vol. 33, No. 9, pp. 41-48.
    29. Tsai, P. (1988-89). “Variable Gauge Repeatability and Reproducibility Study Using The Analysis of Variance Method”. Quality Engineering, Vol. 1, No. 1, pp. 107-115.
    30. Wang, F. K. and Du, T. C. T. (2000). “Using Principal Component Analysis in Process Performance for Multivariate Data”. Omega, Vol. 28, No. 2, pp185-194.
    31. Wang, F. K. and Yang, C. W. (2006). “Applying Principal Component Analysis to a GR&R Study”. Journal of the Chinese Institute of Industrial Engineer 2006 accepted.
    32. Wheeler, D. J. (1992). Problems with Gauge R&R Studies. Transactions of the 46th Annual Quality Congress, pp. 179-185.

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