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다변량 공정능력지수들의 비교분석

Comparison Analysis of Multivariate Process Capability Indices

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주제분류산업공학

초록

Recently, the manufacturing process system in the industrial field has become more and more complex and has been influenced by many and various factors. Moreover, these factors have the dependent correlation rather than independent of each other. Therefore, the statistical analysis has been extended from the univariate method to the multivariate method. The process capability indices have been widely used as statistical tools to assess the manufacturing process performance. Especially, the multivariate process indices need to be enhanced with more useful information and extensive application in the recent industrial fields. The various multivariate process capability indices have been studying by many researchers in recent years. Hence, the purpose of the study is to compare the useful and various multivariate process capability indices through the simulation. Among them, we compare the useful models of several multivariate process capability indices such as , and . These multivariate process capability indices are incorporates both the process variation and the process deviation from target or consider the expected loss caused by the process deviation from target. Through the computational examples, we compare these process capability indices and discuss their usefulness and effectiveness.

Abstract

Recently, the manufacturing process system in the industrial field has become more and more complex and has been influenced by many and various factors. Moreover, these factors have the dependent correlation rather than independent of each other. Therefore, the statistical analysis has been extended from the univariate method to the multivariate method. The process capability indices have been widely used as statistical tools to assess the manufacturing process performance. Especially, the multivariate process indices need to be enhanced with more useful information and extensive application in the recent industrial fields. The various multivariate process capability indices have been studying by many researchers in recent years. Hence, the purpose of the study is to compare the useful and various multivariate process capability indices through the simulation. Among them, we compare the useful models of several multivariate process capability indices such as , and . These multivariate process capability indices are incorporates both the process variation and the process deviation from target or consider the expected loss caused by the process deviation from target. Through the computational examples, we compare these process capability indices and discuss their usefulness and effectiveness.

참고문헌 (12)

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  2. 정영배, "기대손실을 이용한 다변량 공정능력지수", 한국산업경영시스템학회지, 28(4), pp.116~123 (2005), 한국산업경영시스템학회
  3. Drain, D., "Applications of the Upside-Down Normal Loss", IEEE Transactions on Semiconductor Manufacturing, 9(1), pp.143~145 (1996)
  4. 문혜진, "역정규 손실함수를 이용한 다변량 공정능력지수", 한국산업경영시스템학회지, 41(2), pp.174~183 (2018), 한국산업경영시스템학회[DOI]
  5. Mun, H. J., "A Study on Multivariate Process Capability Index using Quality Loss Function", Journal of Society of Korea Industrial and Systems Engineering, 25(2), pp.1~10 (2002)
  6. Pan, J. -N., "New Capability Indices for Evaluating the Performance of Multivariate Manufacturing Processes", Quality and Reliability Engineering International, 26(1), pp.3~15 (2010)
  7. Shahriari, H., "A New Multivariate Process Capability Vector", Quality Engineering, 21(3), pp.290~299 (2009)
  8. Spiring, F. A., "A General Class of Loss Functions with Industrial Applications", Journal of Quality Technology, 30(2), pp.152~162 (1998)
  9. Spiring, F. A., "The Reflected Normal Loss Function", Canadian Journal of Statistics, 21, pp.321~330 (1993)
  10. Taam, W., "A Note on Multivariate Capability Indices", Journal of Applied Statistics, 20, pp.339~351 (1993)
  11. Wang, F. K., "Comparison of three multivariate process capability indices", Journal of Quality Technology, 32(3), pp.263~275 (2000)
  12. Wang, F. K., "Capability index using principal component analysis", Quality Engineering, 11(1), pp.21~27 (1998)

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