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Quality management research by considering multi-response problems in the Taguchi method – a review

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Abstract

Off-line quality control is considered to be an effective approach to improve product quality at a relatively low cost. The Taguchi method is one of the conventional approaches for this purpose. Through this approach, engineers can determine a feasible combination of design parameters such that the variability of a product’s response can be reduced and the mean is close to the desired target. Most previous applications of the Taguchi method only emphasize single-response problems, while the multi-response problems have received relatively little attention. However, several correlated quality characteristics of a product are usually considered for product quality by a consumer. Though a lot of research is being done on this subject, there is ample scope for applying quality by design concepts, especially when dealing with multi-response variables. This paper presents a literature review on solving multi-response problems in the Taguchi method.

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References

  1. Box GEP (1985) Discussion of off-line quality control, parameter design, and the Taguchi methods”. J Qual Technol 17:198–206

    Google Scholar 

  2. Lin CL et al. (2002) Optimization of the EDM Process Based on the Orthogonal Array with Fuzzy Logic and Grey Relational Analysis Method. Int J Adv Manuf Technol 19:271–277

    Article  Google Scholar 

  3. Su C-T, Chang H-H (2000) Optimization of parameter design: an intelligent approach using neural network and simulated annealing. Int J Syst Sci 31(12):1543–1549

    Article  Google Scholar 

  4. Su C-T, Tong L-I (1997) Multi-response robust design by principal component analysis. Total Qual Manage 8(6):409–416

    Article  Google Scholar 

  5. Hsu C-M (2001) Solving multi-response problems through Neural networks and Principal Component Analysis. J Chin Inst Ind Eng 18(5):47–54

    Google Scholar 

  6. Johnson DE (1998) Applied multivariate methods for data analysts. Duxbury, Pacific Grove, CA

  7. Lu D, Antony J (2002) Optimization of multiple responses using a fuzzy-rule based inference system. Int J Prod Res 40(7):1613–1625

    Article  Google Scholar 

  8. Deng J (1989) Introduction to grey system. J Grey Syst 1:1–24

    Google Scholar 

  9. Lin JL and Tarng YS (1998) Optimization of the multi-response process by the Taguchi method with grey relational analysis. J Grey Syst 10(4):355–370

    Google Scholar 

  10. Lin JL et al. (2000) Optimization of the electrical discharge machining process based on the Taguchi method with fuzzy logics. J Mater Process Technol 102:48–55

    Article  Google Scholar 

  11. Joseph J, Pignatiello JR (1993) Strategies for robust multi response quality engineering. Ind Eng Res Develop IIE Trans 25(3):5–15

    Google Scholar 

  12. Anand KN (1996) Development of process technology in wire-cut operation for improving machining quality. Total Qual Manage 7(1):11–28

    Article  Google Scholar 

  13. Zadeh L (1965) Fuzzy sets. Inf Control 8:338–353

    Article  Google Scholar 

  14. Tong L-I, Su C-T (1997) Optimizing Multi-Response Problems in the Taguchi Method by Fuzzy Multiple Attribute Decision Making. Qual Reliability Eng Int 13:25–34

    Article  Google Scholar 

  15. Tong L-I et al. (1997) The optimization of multi response problems in the Taguchi method. Int J Qual Reliability Manage 14(4):367–380

    Article  Google Scholar 

  16. Lin JL. Tarng YS (1998) Optimization of the multi-response process by the Taguchi method with grey relational analysis [electrical discharge machining process]. J Grey Syst 10(4):355–70

    Google Scholar 

  17. Logothetis N, Haigh A (1988) Characterizing and optimizing multi-response processes by the Taguchi method. Qual Reliability Eng Int 4(2):159–69

    Google Scholar 

  18. Collins Holcomb M (1994) Customer service measurement: A methodology for increasing customer value through utilization of the Taguchi strategy. J Bus Logist 15(1):29–52

    Google Scholar 

  19. Reddy PBS et al. (1998) Multi-response optimization - A case study in the Indian plastics industry. Int J Qual Reliability Manage 15(6):646–668

    Article  Google Scholar 

  20. Ross PJ (1996) Taguchi Techniques for Quality Engineering. McGraw-Hill, Singapore

  21. Reddy PBS et al. (1997) Unification of robust design and goal programming for multi-response optimization - a case study. Qual Reliability Eng Int 13:371–383

    Article  Google Scholar 

  22. McCaskey SD, Tsui K-L (1997) Analysis of dynamic robust design experiments. Int J Prod Res 35(6):1561–1574

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Industrial Engineering, Anna University, Chennai, 600 025, India

    R. Jeyapaul, P. Shahabudeen & K. Krishnaiah

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  1. R. Jeyapaul
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  2. P. Shahabudeen
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  3. K. Krishnaiah
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Correspondence to R. Jeyapaul.

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Jeyapaul, R., Shahabudeen, P. & Krishnaiah, K. Quality management research by considering multi-response problems in the Taguchi method – a review. Int J Adv Manuf Technol 26, 1331–1337 (2005). https://doi.org/10.1007/s00170-004-2102-y

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  • DOI: https://doi.org/10.1007/s00170-004-2102-y

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