Skip to main content
Log in

Performance comparison between on-line sensors and control charts in manufacturing process monitoring

  • Published:
IIE Transactions

Abstract

The rapid evolution of sensor technology, using techniques such as lasers, machine vision and pattern recognition, provides the potential to greatly improve the Statistical Process Control (SPC) method for monitoring manufacturing processes. This paper studies the method of using on-line sensors to monitor manufacturing processes and compares that method with the control chart method, a widely used SPC tool. Two separate economic models are formulated for using either a sensor or a control chart to monitor a manufacturing process. Then, the two models are compared in a sensitivity analysis with respect to several process parameters.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Montgomery, D.C. (1991) Introduction to Statistical Quality Control, John Wiley & Sons, New York.

    Google Scholar 

  2. Ho, C. and Case, K.E. (1994) Economic design of control charts: literature review for 1981–1991. Journal of Quality Technology, 26(1), 39–53.

    Google Scholar 

  3. Montgomery, D.C. (1980) The economic design of control charts: a review and literature survey. Journal of Quality Technology, 12, 75–87.

    Google Scholar 

  4. Vance, L.C. (1983) A bibliography of statistical quality control chart techniques, 1970–1980. Journal of Quality Technology, 15, 59–62.

    Google Scholar 

  5. Gibra, I.N. (1975) Recent developments in control chart techniques. Journal of Quality Technology, 7(4), 183–192.

    Google Scholar 

  6. Noble, J.A. (1995) From inspection to process understanding and monitoring: a view on computer vision in manufacturing. Image and Vision Computing, 13(3), 197–214.

    Google Scholar 

  7. Newman, T.S. and Jain, A.K. (1995) A survey of automated visual inspection. Computer Vision and Image Understanding, 61(2), 231–262.

    Google Scholar 

  8. Rao, S.B. (1986) Tool wear monitoring through the dynamics of stable turning. ASME Journal of Engineering for Industry, 108, 108–183.

    Google Scholar 

  9. Pandelidis, I.O. (1992) Machine diagnostics, in Intelligent Design and Manufacturing, Kusiak, A. (ed), John Wiley & Sons, New York. pp. 523–544.

    Google Scholar 

  10. Tlusty, J. and Andrews, G.C. (1983) A critical review of sensors for unmanned machining. Annals of the CIRP, 32(2), 563–572.

    Google Scholar 

  11. Murphy, S.D. (1990) In-process gauging sensors, in In-Process Measurement and Control, Murphy, S.D. (ed), Marcel Dekker, New York, pp. 21–44.

    Google Scholar 

  12. Jwo, W. (1994) Using on-line sensors in statistical process control. Unpublished dissertation, Department of Quantitative Business Analysis, Louisiana State University, Baton Rouge, LA 70803.

    Google Scholar 

  13. Dornfeld, D.A. (1994) In process recognition of cutting states. International Journal of Japan Society of Mechanical Engineers, Series C, 37(4), 638–650.

    Google Scholar 

  14. Chittayil, K., Kumara, S.R.T. and Cohen, P.H. (1994) Acoustic emission sensing for tool wear monitoring and process control in metal cutting, in Handbook of Design, Manufacturing and Automation, Dorf, R. C. and Kusiak, A. (eds), John Wiley and Sons, New York. pp. 698–707.

    Google Scholar 

  15. Dan, L. and Mathew, J. (1990) Tool wear and failure monitoring techniques for turning-a review. International Journal of Machine Tool Manufacturing, 30, 579–598.

    Google Scholar 

  16. Chryssolouris, G., Domroese, M. and Subramaniam, V. (1992) Decision making and sensor synthesis for manufacturing processes, in Proceedings of the 1992 NSF Design and Manufacturing Systems Conference, Atlanta, GA, Jan. 8–20 1992, SME, Dearborn, MI. pp. 995–1000.

    Google Scholar 

  17. Burke, L.I. and Rangwala, S. (1991) Tool condition monitoring in metal cutting: a neural network approach. Journal of Intelligent Manufacturing, 2, 269–280.

    Google Scholar 

  18. Gong, L., Jwo, W. and Tang, K. (1997) Using on-line sensors in statistical process control. Management Science, 43(7), 1017–1029.

    Google Scholar 

  19. Gong, L. and Tang, K. (1997) Monitoring machine operations using on-line sensors. European Journal of Operational Research, 96, 479–492.

    Google Scholar 

  20. Wadsworth, H.M., Stephens, K.S. and Godfrey, A.B. (1986) Modern Methods for Quality Control and Improvement, John Wiley & Sons, New York.

    Google Scholar 

  21. Tang, K. and Tang, J. (1994) Design of screening procedures: a review. Journal of Quality Technology, 26(3), 209–226.

    Google Scholar 

  22. Bilinskis, I. and Mikelsons, A. (1992) Randomized Signal Processing, Prentice Hall, New York.

    Google Scholar 

  23. Tang, K., Gong, L., Williams, W.W. and Jwo, W. (1998) Performance comparison between on-line sensors and control charts in manufacturing process monitoring. Working paper, Department of Information Systems and Decision Sciences, Louisiana State University.

  24. Duncan, A.J. (1986) Quality Control and Industrial Statistics, Irwin, Homewood, III.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

About this article

Cite this article

TANG, K., WILLIAMS, W.W., JWO, W. et al. Performance comparison between on-line sensors and control charts in manufacturing process monitoring. IIE Transactions 31, 1181–1190 (1999). https://doi.org/10.1023/A:1007652330900

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/A:1007652330900

Keywords

Navigation