Optimization of Multi-Response Problems Using Taguchi's Quality Loss Function Based on Grey Relational Grade

Abstract:

This article presents an approach which first combines grey relational grade and weighted quality loss function to convert the values of multiple responses obtained from each of the Taguchi orthogonal designed experiments into a single performance evaluation value. Then the analysis of variance (ANOVA) and the SN ratio based Taguchi analysis are conducted to find out respectively the relative importance of the process parameters and a set of near optimal parameter settings. A validation experiment is then conducted to confirm the finding. A case of multi-response turning process optimization is used to illustrate the proposed approach. Two sets of weights for the total quality loss were applied and the results were compared. The effectiveness of this approach was demonstrated by the improvement of turning performance in the both weight settings.

• Periodical: Advanced Materials Research (Volumes 154-155)
• Pages: 1643-1654
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.154-155.1643
• Online since: October 2010
• Authors: Wei Liang Ku, Han Ming Chow, Jiang Long Lin, Kun Ling Wu, Tsong Shin Sheu
• Keywords: Grey Relational Grade, Multiple Criteria Analysis, Quality Loss Function

References

[1] G. Taguchi: Introduction to Quality Engineering (Asian Productivity Organization, Tokyo 1990).

[2] P. J. Ross: Taguchi Techniques for Quality Engineering (McGraw-Hill, New York 1988).

[3] M. S. Phadke: Quality Engineering Using Robust Design (Prentice Hall, New Jersey 1989).

[4] N. Logothetis, and A. Haigh: Characterizing , Quality and Reliability Engineering International, Vol. 4 (1988), P. 159.

[5] E. A. Elsayed and A. Chen: International Journal of Production Research, Vol. 31/5 (1993) , p.1117.

[6] Pradeep Kumar, P. B. barua and J. L. Gaindhar: Quality and Reliability Engineering International, Vol. 16 (2000), p.475.

[7] J. Antony: Int. J. Adv. Manuf. Technol. , Vol. 17 (2001), p.134.

[8] J. Antony: Quality and Reliability Engineering International, Vol. 16 (2000), p.3.

[9] V. N. , Gaitonde, S. R. Karnik, B. T. Achyutha and B. Siddeswarappa: Indian Journal of Engineering and Materials Sciences, Vol. 13 (2006), p.484.

[10] Y. S. Trang, W. H. Yang and S. C. Juang: Int. J. Adv. Manuf. Technol., Vol. 16, (2000) , p.688.

[11] C. L. Lin, J. L. Lin and T. C. Ko: Int. J. Adv. Manuf. Technol., Vol. 19 (2002), p.271.

[12] J. J. Pignatiello: IIE Transactions, Vol. 25 (1993), p.5.

[13] E. A. Elsayed and A. Chen: International Journal of Production Research, Vol. 31 (1993), p.1117.

[14] L. I. Tong, C. T. Su and C. H. Wang: International Journal of Quality and Reliability Management, Vol. 14 (1997), p.367.

[15] J. Deng: Journal of Grey System, Vol. 1/1 (1989), p.1.

[16] P. S. , Kao and H. Hocheng: J. Mater. Process Technol., Vol. 140, (2003), p.255.

[17] J. Deng: Systems and Control Letters, Vol. 5 (1982) , p.288.

[18] Z. Wang, L. Zhu and J. H. Wu: Journal of Grey System, Vol. 8/1 (1996), p.73.

[19] M. C. Shaw: Metal cutting principle (Oxford University Press, New York 1984).

[20] R. A. Fisher: Statistical Methods for Research Worker (Oliver & Boyd, London 1925).