• 46 Accesses

Abstract

This chapter discusses the fundamental questions: the essence of robust design, and the fundamental requirements to conduct the robust design.

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

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
EUR 29.95
Price includes VAT (Germany)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
EUR 111.27
Price includes VAT (Germany)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
EUR 149.79
Price includes VAT (Germany)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. V.N. Nair, Taguchi’s parameter design: a panel discussion. Technometrics 34, 127–161 (1992)

    Article  MathSciNet  Google Scholar 

  2. G. Taguchi, Introduction to Quality Engineering (UNIPUB/Kraus International: White Plains, NY, 1986)

    Google Scholar 

  3. T.J. Robinson, C.M. Borror, R.H. Myers, Robust parameter design: a review. Qual. Reliab. Engng. Int. 20, 81–101 (2004)

    Article  Google Scholar 

  4. G.E.P. Box, R.D. Meyer, Dispersion effects from fractional designs. Technometrics 28(1), 19–27 (1986)

    Article  MathSciNet  Google Scholar 

  5. G.G. Vining, R.H. Myers, Combining Taguchi and response surface philosophies: a dual response approach. J. Qual. Technol. 22(1), 38–45 (1990)

    Article  Google Scholar 

  6. W.J. Welch, T.K. Yu, S.M. Kang, J. Sacks, Computer experiments for quality control by parameter design. J. Qual. Technol. 22, 15–22 (1990)

    Article  Google Scholar 

  7. J.A. Nelder, Y. Lee, Generalized linear models for the analysis of Taguchi-type experiments. Appl. Stochastic Models Data Anal. 7, 107–120 (1991)

    Article  Google Scholar 

  8. J. Engel, A.F. Huele, A generalized linear modeling approach to robust design. Technometrics 38, 365–373 (1996)

    Article  Google Scholar 

  9. M. Zheng, J. Yu, H. Teng, Y. Cui, Y. Wang, Probability-Based Multi-Objective Optimization for Material Selection, 2nd edn. (Springer, Singapore, 2023)

    Book  Google Scholar 

  10. M. Teruo, Taguchi Methods, Benefits, impacts, mathematics, statistics, and applications (ASME Press, New York, pp. 47–204, 2011)

    Google Scholar 

  11. V.N. Nair, D. Pregibon, Analyzing dispersion effects from replicated factorial experiments. Technometrics 30, 247–257 (1988)

    Article  MathSciNet  Google Scholar 

  12. G.E.P. Box, Signal-to-noise ratios, performance criteria, and transformations. Technometrics 30, 1–17 (1988)

    Article  MathSciNet  Google Scholar 

  13. E.D. Castillo, D.C. Montgomery, A nonlinear programming solution to the dual response problem. J. Qual. Technol. 25(3), 199–204 (1995)

    Article  Google Scholar 

  14. D.K.J. Lin, W. Tu, Dual response surface optimization. J. Qual. Technol. 27(1), 34–39 (1995)

    Article  MathSciNet  Google Scholar 

  15. A.F.C. Karen, P.R. Nelson, Dual response optimization via direct function minimization. J. Qual. Technol. 28(3), 26–30 (1996)

    Google Scholar 

  16. K.J. Kim, D.K.J. Lin, Dual response surface optimization: a fuzzy modeling approach. J. Qual. Technol. 30(1), 1–10 (1998)

    Article  MathSciNet  Google Scholar 

  17. R. Ding, K.J. Lin, D. Wei, Dual response surface optimization: a weighted MSE approach. Qual. Eng. 16(3), 377–385 (2004)

    Article  Google Scholar 

  18. J. Kovach, B.R. Cho, J. Antony, Development of a variance prioritized multi-response robust design framework for quality improvement. Int. J. Qual. Reliab. Manag. 26(4), 380–396 (2009)

    Article  Google Scholar 

  19. I.J. Jeong, K.J. Kim, D.K.J. Lin, Bayesian analysis for weighted mean squared error in dual response surface optimization. Qual. Reliab. Eng. Int. 26(5), 417–430 (2010)

    Article  Google Scholar 

  20. D.H. Lee, I.J. Jeong, K.J. Kim, A posterior preference articulation approach to dual-response-surface optimization. IIE Transaction. 42(2), 161–171 (2010)

    Article  Google Scholar 

  21. Z. He, Y. H. Ma, Y. Zhao, Multi-response robust optimization design based on Taguchi process capability index and entropy weight theory. Chinese Agric. Mech. 29(3), 33–36 (2008). 1006-7205(2008)03-0033-04

    Google Scholar 

  22. L. Ouyang, Y. Ma, J. Wang, F. Wu, Robust design based on entropy weight and dual response surface. J. Indus. Eng. Eng. Manag. 28(2), 191–195 (2014). https://doi.org/10.13587/j.cnki.jieem.2014.02.015

    Article  Google Scholar 

  23. O. K¨oksoy, N. Doganaksoy, Joint optimization of mean and standard deviation using response surface methods. J. Qual. Technol. 35, 239–252 (2003)

    Google Scholar 

  24. R.H. Myers, A.I. Khuri, G.G. Vining, Response surface alternatives to the Taguchi robust parameter design approach. Am. Stat. 46, 131–139 (1992)

    Article  Google Scholar 

  25. R.H. Myers, D.C. Montgomery, A tutorial on generalized linear models. J. Qual. Technol. 29, 274–291 (1997)

    Article  Google Scholar 

  26. M. Zheng, J. Yu, Probabilistic approach for robust design with orthogonal experimental methodology in case of target the best. J. Umm Al-Qura Univ. Eng. Archit. 15(1), 55–59 (2024). https://doi.org/10.1007/s43995-023-00040-2

    Article  Google Scholar 

  27. M. Arvidsson, I. Gremy, Principles of robust design methodology. Qual. Reliab. Engng. Int. 24, 23–35 (2008)

    Article  Google Scholar 

  28. G. Box, S. Bisgaard, Statistical tools for improving designs. Mech. Eng. 110, 32–40 (1988)

    Google Scholar 

  29. C. Hirsch, D. Wunsch, J. Szumbarski, L. Łaniewski-Wołłk, J. Pons-Prats, Uncertainty Management for Robust Industrial Design in Aeronautics (Springer, Cham, Switzerland, 2019)

    Book  Google Scholar 

  30. J. Mukherjee, I.N. Kar, S. Mukherjee, Adaptive Robust Control for Planar Snake Robots (Springer, Cham, Switzerland, 2021)

    Book  Google Scholar 

  31. S. Salomon, Active Robust Optimization: Optimizing for Robustness of Changeable Products (Springer, Cham, Switzerland, 2019)

    Book  Google Scholar 

  32. N.S. Hadjidimitriou, A. Frangioni, T. Koch, A. Lodi, Mathematical Optimization for Efficient and Robust Energy Networks (Springer, Cham, Switzerland, 2021)

    Book  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Maosheng Zheng .

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Zheng, M., Yu, J. (2024). Intrinsic Essence of Robust Design. In: Robust Design and Assessment of Product and Production by Means of Probabilistic Multi-objective Optimization. Springer, Singapore. https://doi.org/10.1007/978-981-97-2661-5_1

Download citation

  • DOI: https://doi.org/10.1007/978-981-97-2661-5_1

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-97-2660-8

  • Online ISBN: 978-981-97-2661-5

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Navigation