Skip to main content
Log in

Development of an experiment-based robust design paradigm for multiple quality characteristics using physical programming

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

The well-known quality improvement methodology, robust design, is a powerful and cost-effective technique for building quality into the design of products and processes. Although several approaches to robust design have been proposed in the literature, little attention has been given to the development of a flexible robust design model. Specifically, flexibility is needed in order to consider multiple quality characteristics simultaneously, just as customers do when judging products, and to capture design preferences with a reasonable degree of accuracy. Physical programming, a relatively new optimization technique, is an effective tool that can be used to transform design preferences into specific weighted objectives. In this paper, we extend the basic concept of physical programming to robust design by establishing the links of experimental design and response surface methodology to address designers’ preferences in a multiresponse robust design paradigm. A numerical example is used to show the proposed procedure and the results obtained are validated through a sensitivity study.

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. Taguchi GW (1986) Introduction to quality engineering. Krauss International Publications, White Plains, New York

    Google Scholar 

  2. Taguchi GW (1987) Systems of experimental design: engineering methods to optimize quality and minimize cost. Quality Resources, White Plains, New York

    Google Scholar 

  3. Taguchi GW, Wu YI (1985) Introduction to off-line quality control. American Supplier Institute, Dearborn, Michigan

    Google Scholar 

  4. Phadke MS (1989) Quality engineering using robust design. Prentice Hall, Englewood Cliffs, New Jersey

    Google Scholar 

  5. Kackar RN (1985) Off-line quality control, parameter design, and the Taguchi method. J Qual Tech 17(4):176–188

    Google Scholar 

  6. Kackar RN (1986) Taguchi’s quality philosophy: analysis and commentary. Qual Prog 19(12):21–29

    Google Scholar 

  7. Leon RV, Shoemaker AC, Kackar RN (1987) Performance measures independent of adjustment: an explanation and extension of Taguchi’s signal-to-noise ratios. Technometrics 29(3):253–285

    Article  MATH  MathSciNet  Google Scholar 

  8. Box GEP (1988) Signal-to-noise ratios, performance criteria, and transformations. Technometrics 30(1):1–17

    Article  MATH  MathSciNet  Google Scholar 

  9. Box GEP, Bisgaard S, Fung C (1988) An explanation and critique of Taguchi’s contributions to quality engineering. Int J Relia Mgt 4(2):123–131

    Google Scholar 

  10. Pignatiello JJ Jr, Ramberg JS (1991) Top ten triumphs and tragedies of Genichi Taguchi. Qual Eng 4(2):211–225

    Article  Google Scholar 

  11. Nair VN (1992) Taguchi’s parameter design: a panel discussion. Technometrics 34(2):127–161

    Article  MathSciNet  Google Scholar 

  12. Tsui KL (1992) An overview of Taguchi method and newly developed statistical methods for robust design. IIE Trans 24(5):44–57

    Article  MathSciNet  Google Scholar 

  13. Myers RH (1999) Response surface methodology—current status and future directions. J Qual Tech 31(1):30–44

    Google Scholar 

  14. Vining GC, Myers RH (1990) Combining Taguchi and response surface philosophies: a dual response approach. J Qual Tech 22(1):38–45

    Google Scholar 

  15. del Castillo E, Montgomery DC (1993) A nonlinear programming solution to the dual response problem. J Qual Tech 25(3):199–204

    Google Scholar 

  16. Copeland KAF, Nelson PR (1996) Dual response optimization via direct function minimization. J Qual Tech 28(3):331–336

    Google Scholar 

  17. Cho BR (1994) Optimization issues in quality engineering. PhD thesis, University of Oklahoma, Oklahoma

  18. Lin DKJ, Tu W (1995) Dual response surface optimization. J Qual Tech 27(1):34–39

    Google Scholar 

  19. Kim YJ, Cho BR (2000) Economic consideration on parameter design. Qual Reliab Eng Int 16:501–514

    Article  Google Scholar 

  20. Parkinson DB (2000) Robust design employing a genetic algorithm. Qual Reliab Eng Int 16(3):201–208

    Article  MathSciNet  Google Scholar 

  21. Brenneman WA, Myers WR (2003) Robust parameter design with categorical noise variables. J Qual Tech 35(4):335–341

    Google Scholar 

  22. Köksoy O, Doganaksoy N (2003) Joint optimization of mean and standard deviation using response surface methods. J Qual Tech 35(3):239–252

    Google Scholar 

  23. Miro-Quesada G, del Castillo E (2004) Two approaches for improving the dual response method in robust parameter design. J Qual Tech 36(2):154–168

    Google Scholar 

  24. Shin SM, Cho BR (2005) Bias-specified robust design optimization and its analytical solutions. Comp Ind Eng 48(1):129–140

    Article  Google Scholar 

  25. Harrington EC (1965) The desirability function. Ind Qual Cont 21(10):494–498

    Google Scholar 

  26. Derringer G, Suich R (1980) Simultaneous optimization of several response variables. J Qual Tech 12(4):214–219

    Google Scholar 

  27. Khuri AI, Conlon M (1981) Simultaneous optimization of multiple responses represented by polynomial regression functions. Technometrics 23(4):363–375

    Article  MATH  Google Scholar 

  28. Pignatiello JJ (1993) Strategies for robust multi-response quality engineering. IIE Trans 25(3):5–15

    Article  Google Scholar 

  29. Messac A (1996) Physical programming: effective optimization for computational design. AIAA J 34(1):149–158

    MATH  Google Scholar 

  30. Messac A (2000) From the dubious construction of objective functions to the application of physical programming. AIAA J 38(1):155–163

    Article  Google Scholar 

  31. Messac A, Gupta SM, Akbulut B (1996) Linear physical programming: a new approach to multiple objective optimization. Trans Oper Res 8(1):39–59

    Google Scholar 

  32. Chen W, Sahai A, Messac A, Sundararaj GJ (2000) Exploration of the effectiveness of physical programming in robust design. J Mech Des 122(2):155–163

    Article  Google Scholar 

  33. Messac A, Ismail-Yahaya A (2001) Multiobjective robust design using physical programming. Struc Multidiscip Opt 23(5):357–371

    Article  Google Scholar 

  34. Logothetis N, Haigh A (1988) Characterizing and optimizing multi-response processes by Taguchi method. Qual Relia Eng Int 4(2):159–169

    Article  Google Scholar 

  35. Elsayed EA, Chen A (1993) Optimal levels of process parameters for products with multiple characteristics. Int J Prod Res 31(5):1117–1132

    Article  Google Scholar 

  36. Ames AE, Mattucci N, MacDonald S, Szonyi G, Hawkins DM (1997) Quality loss functions for optimization across multiple response surfaces. J Qual Tech 29(3):339–346

    Google Scholar 

  37. Derringer GC (1994) A balancing act: optimizing a product’s properties. Qual Prog 27(6):51–58

    Google Scholar 

  38. Kapur KC, Cho BR (1996) Economic design of the specification region for multiple quality characteristics. IIE Trans 28(3):237–248

    Article  Google Scholar 

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

    Article  Google Scholar 

  40. Tong L-I, Su C-T (1997) Optimizing multi-response problems in the Taguchi method by fuzzy multiple attribute decision making. Qual Relia Eng Int 13(1):25–34

    Article  Google Scholar 

  41. Tong L-I, Su C-T, Wang C-H (1997) The optimization of multi-response problems in the Taguchi method. Int J Qual and Relia Mgt 14(4):367–380

    Article  Google Scholar 

  42. Chen L-H (1997) Designing robust products with multiple quality characteristics. Comp Oper Res 24(10):937–944

    Article  MATH  Google Scholar 

  43. Kim K, Lin DKJ (1998) Dual response surface optimization: a fuzzy modeling approach. J Qual Tech 30(1):1–10

    Google Scholar 

  44. Vining GG (1998) A compromise approach to multiresponse optimization. J Qual Tech 30(3):309–313

    Google Scholar 

  45. Tsui KL (1999) Robust design optimization for multiple characteristic problems. Int J Prod Res 37(2):433–445

    Article  MATH  MathSciNet  Google Scholar 

  46. Chiao C-H, Hamada M (2001) Analyzing experiments with correlated multiple responses. J Qual Tech 33(4):451–465

    Google Scholar 

  47. Kim YJ, Cho BR (2002) Development of priority-based robust design. Qual Eng 14(3):355–363

    Article  Google Scholar 

  48. Tang LC, Xu K (2002) A unified approach for dual response surface optimization. J Qual Tech 34(4):437–447

    MathSciNet  Google Scholar 

  49. Lin CL, Lin JL, Ko TC (2002) Optimization of the EDM process based on the orthogonal array with fuzzy logic and grey relational analysis method. Int J Adv Manuf Tech 19(4):271–277

    Article  Google Scholar 

  50. 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 

  51. Romano D, Varetto M, Vicario G (2004) Multiresponse robust design: a general framework based on combined array. J Qual Tech 36(1):27–37

    Google Scholar 

  52. Wu F-C, Chyu C-C (2004) Optimization of robust design for multiple quality characteristics. Int J Prod Res 42(2):337–354

    Article  MATH  Google Scholar 

  53. Hillier FS, Lieberman GJ (2001) Introduction to operations research. McGraw-Hill, New York

    Google Scholar 

  54. Bourquin J, Schmidli H, van Hoogevest P, Leuenberger H (1998) Pitfalls of artificial neural networks (ANN) modelling technique for data sets containing outlier measurements using a study on mixture properties of a direct compressed dosage form. Eur J Pharm Sci 7(1):17–28

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Byung Rae Cho.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Kovach, J., Cho, B.R. & Antony, J. Development of an experiment-based robust design paradigm for multiple quality characteristics using physical programming. Int J Adv Manuf Technol 35, 1100–1112 (2008). https://doi.org/10.1007/s00170-006-0792-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-006-0792-z

Keywords

Navigation