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Integrating multivariate engineering process control and multivariate statistical process control

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Abstract

Multivariate engineering process control (MEPC) and multivariate statistical process control (MSPC) are two strategies for quality improvement that have developed independently. MEPC aims to minimize variability by adjusting process variables to keep the process output on target. On the other hand, MSPC aims to reduce variability by monitoring and eliminating assignable causes of variation. In this paper, the use of MEPC alone is compared to using the MEPC coupled with MSPC. We use simulations to evaluate the average run lengths (ARL) and the averages of the performance measure. The simulation results show that the use of both MEPC and MSPC can always outperform the use of either alone. To detect small sustained shifts of the mean vector, combing MEPC with a multivariate generally weighted moving average (MGWMA) chart (MEPC/MGWMA) is more sensitive than the MEPC/multivariate exponentially weighted moving average (MEWMA) chart and MEPC/Hotelling’s χ2 chart. An example of the application, based on the proposed method, is also given.

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References

  1. Box GEP, Kramer T (1992) Statistical process monitoring and feedback adjustment –a discussion. Technometrics 34:251–285

    Article  MathSciNet  Google Scholar 

  2. MacGregor JF (1988) On-line statistical process control. Chem Eng Process 10:21–31

    Google Scholar 

  3. Montgomery DC, Keats JB, Runger GC, Messina WS (1994) Integrating statistical process control and engineering process control. J Qual Tech 26:79–87

    Google Scholar 

  4. Box GEP, Jenkins GM, Reinsel GC (1994) Time series analysis: forecasting and control. Prentice-Hall, Englewood Cliffs, NJ

    Google Scholar 

  5. Keats JB, Montgomery DC, Runger GC, Messina WS (1992) Integrating statistical process control and feedback control for continuous processes. Technical Report 92–26, Qual Reliab Eng

  6. Sachs E, Hu A, Ingolfsson A (1995) Run by run process control: combining SPC and feedback control. IEEE Trans Semiconduct Manuf 8:26–43

    Article  Google Scholar 

  7. Tsung F, Shi J (1999) Integrated design of run-to-run PID controller and SPC monitoring for process disturbance rejection. IIE Trans 31:517–527

    Article  Google Scholar 

  8. Tsung F (2000) Statistical monitoring and diagnosis of automatic controlled processes using dynamic PCA. Int J Prod Res 38:625–637

    Article  MATH  Google Scholar 

  9. Pan R, del Castillo E (2003) Integration of sequential process adjustment and process minitoring techniques. Qual Reliab Eng Int 19:371–386

    Article  Google Scholar 

  10. Raich A, Cinar A (1996) Statistical process monitoring and disturbance diagnosis in multivariate continuous process. AIChE J 42:995–1009

    Article  Google Scholar 

  11. Tseng ST, Chou RJ, Lee SP (2002) A study on a multivariate EWMA controller. IIE Trans 34:541–549

    Article  Google Scholar 

  12. Ingolfsson A, Sachs E (1993) Stability and sensitivity of an EWMA controller. J Qual Tech 25:271–287

    Google Scholar 

  13. Bulter SW, Stefani JA (1994) Supervisory run-to-run control of polysilicon gate etch using in situ ellipsometry. IEEE Trans Semiconduct Manuf 7:193–201

    Article  Google Scholar 

  14. del Castillo E, Rajagopal R (2002) A multivariate double EWMA process adjustment scheme for drifting processes. IIE Trans 34:1055–1068

    Article  Google Scholar 

  15. Hotelling H (1947) Multivariate quality control – illustrated by the air testing of sample bombsights. In: Eisenhart C, et al. (eds) Techniques of statistical analysis, McGraw-Hill, New York, pp 111–184

  16. Montgomery DC (2001) Introduction to statistical quality control. Wiley, New York

  17. Lowry CA, Woodall WH, Champ CW, Rigdon SE (1992) A multivariate exponentially weighted moving average control chart. Technometrics 34:46–53

    Article  MATH  Google Scholar 

  18. Sheu SH, Lin TC (2003) The generally weighted moving average control chart for detecting small shifts in the process mean. Qual Eng 16:209–231

    Article  Google Scholar 

  19. Ross SM (1990) A course in simulation. Macmillan, New York

  20. Johnson RA, Wichern DW (2002) Applied multivariate statistical analysis. Prentice-Hall, Englewood Cliffs, NJ, pp 163–164

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Authors and Affiliations

  1. Department of Industrial Engineering and Management, St. John’s University, Taipei, Taiwan

    Ling Yang

  2. Department of Industrial Management, National Taiwan University of Science and Technology, Taipei, Taiwan

    Shey-Huei Sheu

Authors
  1. Ling Yang
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  2. Shey-Huei Sheu
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Correspondence to Shey-Huei Sheu.

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Yang, L., Sheu, SH. Integrating multivariate engineering process control and multivariate statistical process control. Int J Adv Manuf Technol 29, 129–136 (2006). https://doi.org/10.1007/s00170-004-2494-8

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  • DOI: https://doi.org/10.1007/s00170-004-2494-8

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