EDP Sciences logo
Submit your paper
Search
Menu
All issues Volume 26 (2025) Mechanics & Industry, 26 (2025) 1 References
Open Access
Issue
Mechanics & Industry
Volume 26, 2025
Article Number 1
Number of page(s) 11
DOI https://doi.org/10.1051/meca/2024034
Published online 03 January 2025
  1. W. Li, L. Gao, M. Xiao, Multidisciplinary robust design optimization under parameter and model uncertainties, Eng. Optim. 52, 426–445 (2020) [CrossRef] [MathSciNet] [Google Scholar]
  2. J. Wauters, ERGO: a new robust design optimization technique combining multi-objective bayesian optimization with analytical uncertainty quantification, J. Mech. Des. 144, 031702 (2022) [CrossRef] [Google Scholar]
  3. Y. Yi, W. Li, M. Xiao, L. Gao, A set strategy approach for multidisciplinary robust design optimization under interval uncertainty, Adv. Mech. Eng. 11, 1–17 (2019) [Google Scholar]
  4. G. Li, Z. Liu, G. Zhao, Z. Zeng, Reliability-based robust design optimization in consideration of manufacturing tolerance by multi-objective evolutionary optimization with repair algorithm, Int. J. Comput. Methods 18, 2150005 (2021) [CrossRef] [Google Scholar]
  5. S. Wang, Q. Li, G.J. Savage, Reliability-based robust design optimization of structures considering uncertainty in design variables, Math. Probl. Eng. 4, 1–8 (2015) [CrossRef] [Google Scholar]
  6. J. Zhang, H. Du, D. Xue, P. Gu, Robust design approach to the minimization of functional performance variations of products and systems, Front. Mech. Eng. 16, 379–392 (2021) [CrossRef] [Google Scholar]
  7. A. Al-Refaie, M.H.C. Li, B.C. Chang, I. Jalham, Application of the Taguchi approach to improve performance of the alignment-layer printing process in liquid crystal displays. Proc. Inst. Mech. Eng. B 226, 1241–1248 (2012) [CrossRef] [Google Scholar]
  8. M.S. Phadke, Quality Engineering Using Robust Design (Prentice Hall PTR, 1995) [Google Scholar]
  9. A.F. Shahraki, R. Noorossana, Reliability-based robust design optimization: a general methodology using genetic algorithm, Comput. Ind. Eng. 74, 199–207 (2014) [CrossRef] [Google Scholar]
  10. C.M. Creveling, Tolerance Design: A Handbook for Developing Optimal Specifications (Addison Wesley Longman, Inc., 1995) [Google Scholar]
  11. W.Y. Fowlkes, C.M. Creveling, Engineering Methods for Robust Product Design: Using the Taguchi Method in Technology and Product Development (Prentice Hall, 1995) [Google Scholar]
  12. G. Taguchi, Introduction to quality engineering, Asian Product. Organ. 1–24 (1990) [Google Scholar]
  13. R.V. Leon, A.C. Shoemaker, R.N. Kacker, Performance measures independent of adjustment: an explanation and extension of Taguchi's signal-to-noise ratios, Technometrics 29, 253–265 (1987) [CrossRef] [MathSciNet] [Google Scholar]
  14. S. Maghsoodloo, C.L. Chang, Quadratic loss functions and signal-to- noise ratios for a bivariate response, J. Manufactur. Syst. 20, 1–12 (2001) [CrossRef] [Google Scholar]
  15. K.J. Kim, D.K. Lin, Simultaneous optimization of mechanical prop erties of steel by maximizing exponential desirability functions, J. Royal Stat. Soc.: Ser. C 49, 311–325 (2000) [CrossRef] [Google Scholar]
  16. L.I. Tong, C.T. Su, Optimizing multi-response problems in the Taguchi method by fuzzy multiple attribute decision-making, Qual. Reliab. Eng. Int. 13, 25–34 (1997) [CrossRef] [Google Scholar]
  17. E. Canessa, G. Bielenberg, H. Allende, Robust design in multi-objective systems using Taguchi's parameter design approach and a Pareto genetic algorithm, Revista Facultad de Ingenieŕıa Universidad de Antio-quia 73–86 (2014) [CrossRef] [Google Scholar]
  18. S. Pal, S.K. Gauri, Multi-response optimization using multiple regression-based weighted signal-to-noise ratio (mrwsn), Qual. Engineering 22, 336–350 (2010) [CrossRef] [Google Scholar]
  19. A. Al-Refaie, W. Al-Alaween, A. Diabat, M.H. Li, Solving dynamic systems with multi-responses by integrating desirability function and data envelopment analysis, J. Intell. Manufactur. 28, 387–403 (2017) [CrossRef] [Google Scholar]
  20. H.H. Chang, Y.K. Chen, Neuro-genetic approach to optimize parameter design of dynamic multiresponse experiments, Appl. Soft Comput. 11, 436–442 (2011) [Google Scholar]
  21. H.H. Chang, A data mining approach to dynamic multiple responses in Taguchi experimental design, Expert Syst. Appl. 35, 1095–1103 (2008) [CrossRef] [Google Scholar]
  22. V.M. Pillai, I.B. Hunagund, K.K. Krishnan, Design of robust layout for dynamic plant layout problems, Comput. Ind. Eng. 61, 813–823 (2011) [CrossRef] [Google Scholar]
  23. S. Gauri, Optimization of multi-response dynamic systems using principal component analysis (PCA)-based utility theory approach, Int. J. Ind. Eng. Comput. 5, 101–114 (2014) [Google Scholar]
  24. L.I. Tong, C.H. Wang, C.C. Chen, C.T. Chen, Dynamic multiple responses by ideal solution analysis, Eur. J. Oper. Res. 156, 433–444.(2004) [CrossRef] [Google Scholar]
  25. C.H. Wang, Dynamic multi-response optimization using principal component analysis and multiple criteria evaluation of the grey relation model, Int. J. Adv. Manufactur. Technol. 32, 617–624 (2007) [CrossRef] [Google Scholar]
  26. C.H. Wang, L.I. Tong, Optimization of dynamic multi-response problems using grey multiple attribute decision-making, Qual. Eng. 17, 1–9 (2004) [CrossRef] [Google Scholar]
  27. M. Bashiri, A. Amiri, M. Jalili, Taguchi design optimization using multivariate process capability index, J. Ind. Syst. Eng. 9, 57–78 (2016) [Google Scholar]
  28. K.L. Hsieh, L.I. Tong, H.P. Chiu, H.Y. Yeh, Optimization of a multi- response problem in Taguchi's dynamic system, Comput. Ind. Eng. 49, 556–571 (2005) [CrossRef] [Google Scholar]
  29. F.C. Wu, Robust design of mixing static and dynamic multiple quality characteristics, World J. Eng. Technol. 3, 72–77 (2015) [CrossRef] [Google Scholar]
  30. A. Villa-Murillo, A. Carríon, A. Sozzi, Forest-genetic method to optimize parameter design of multiresponse experiment, Intelig. Artif. 23, 9–25 (2020) [Google Scholar]
  31. S. Pal, S.K. Gauri, Optimization of multi-response dynamic systems integrating multiple regression and Taguchi's dynamic signal-to-noise ratio concept, Int. J. Eng. Sci. Technol. 9, 16–33 (2017) [CrossRef] [Google Scholar]
  32. N.K. Sharma, E.A. Cudney, S.M. Corns, Integration of dynamic multi-response systems using the product of normalised squared-bias and variance, Int. J. Qual. Eng. Technol. 3, 108–123 (2012) [CrossRef] [Google Scholar]
  33. J. Wang, Y. Ma, G. Su, Robust parameter design of dynamic multi- response system: a new integrated method, Asian J. Qual. 12, 67–79 (2011) [CrossRef] [Google Scholar]
  34. M.A. Rezgui, A. Trabelsi, Use of the stochastic frontier and the grey relational analysis in robust design of multi-objective problems, Concurr. Eng. 28, 110–129 (2020) [CrossRef] [Google Scholar]
  35. A. Trabelsi, M.A. Rezgui, Robust design of processes and products using the mathematics of the stochastic frontier (SF), Int. J. Adv. Manufactur. Technol. 106, 2829–2841 (2020) [CrossRef] [Google Scholar]
  36. T.J. Coelli, D.S.P. Rao, C.J. O'Donnell, G.E. Battese, An Introduction to Efficiency and Productivity Analysis (Springer Science & Business Media, 2005) [Google Scholar]
  37. D. Aigner, C.K. Lovell, P. Schmidt, Formulation and estimation of stochastic frontier production function models, J. Econometr. 6, 21–37 (1977) [CrossRef] [Google Scholar]
  38. W. Meeusen, J. van Den Broeck, Efficiency estimation from Cobb-Douglas production functions with composed error, Int. Econ. Rev. 18, 435–444 (1977) [CrossRef] [Google Scholar]
  39. P. Schmidt, C.K. Lovell, Estimating stochastic production and cost frontiers when technical and allocative inefficiency are correlated, J. Econometr. 13, 83–100 (1980) [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.