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All issues Volume 13 (2022) Int. J. Metrol. Qual. Eng., 13 (2022) 5 References
Open Access
Issue
Int. J. Metrol. Qual. Eng.
Volume 13, 2022
Article Number 5
Number of page(s) 11
DOI https://doi.org/10.1051/ijmqe/2022005
Published online 08 June 2022
  1. M. Mu, T. Hong, Application of DOE on quality improvement in magnetic system assembly of electronic meter, Dev. Innov. Mach. Electr. Prod. 23, 71–73 (2010) [Google Scholar]
  2. Y.Q. Wang, S.H. Fan, M.M. Pan, Application of process quality improvement in assembly of magnetic system based on minitab, Mach. Des. Manufact. 01, 62–64 (2012) [Google Scholar]
  3. C.J. Guo, Magnetic Device Assembly Quality Control based on DMAIC Mode, Hangzhou Dianzi University, 2016 [Google Scholar]
  4. S. Goetz, B. Schleich, Integration of robust and tolerance design in early stages of the product development process, Res. Eng. Des. 31, 157–173 (2020) [CrossRef] [Google Scholar]
  5. A.B. Nellippallil, P. Mohan et al., An inverse, decision-based design method for robust concept exploration, J. Mech. Des. 142, 15 (2020) [CrossRef] [Google Scholar]
  6. A. Parnianifard, A.S. Azfanizam et al., An overview on robust design hybrid metamodeling: advanced methodology in process optimization under uncertainty, Int. J. Ind. Eng. Comput. 1, 1–32 (2018) [Google Scholar]
  7. B. Najlawi, M. Nejlaoui et al., Multi-objective robust design optimization of a sewing mechanism under uncertainties, J. Intell. Manufactur. 30, 783–794 (2019) [CrossRef] [Google Scholar]
  8. S. Kitayama, K. Kenta et al., Optimization of variable blank holder force trajectory by sequential approximate optimization with RBF network, Int. J. Adv. Manufactur. Technol. 61, 1067–1083 (2012) [CrossRef] [Google Scholar]
  9. D. Bekar, E. Acar, F. Ozer et al., Robust spring back optimization of a dual phase steel seven-flange die assembly, Struct. Multidiscipl. Optim. 46, 425–444 (2012) [Google Scholar]
  10. J.W. Wu, H.M. Song, L.Q. Wan, et al., Robust design method for non-normal distribution multiple quality characteristics of MEMS product, Comput. Integr. Manufactur. Syst. 26, 1771–1781 (2020) [Google Scholar]
  11. J. Chen, C. Liu, Z.Y. Yang et al., Robust optimization design of engine mounting system based on Six Sigma method, J. Hefei Univ. Technol. 40, 1469–1473 (2017) [Google Scholar]
  12. R.M. Yuan, Y.G. Lv, W.W. Li et al., Optimization design of manganese copper shunt based on robust design theory, J. Electr. Measur. Instru. 33, 188–195 (2019) [Google Scholar]
  13. Y.B. Shui, H.J. Rong, W.P. Ding, Anti abnormal noise robustness design of automotive hydraulic shock absorber, Chin. Hydraul. Pneum. (09), 20–25 (2018) [Google Scholar]
  14. Y.H. Li, H.J. Li, J. Wang et al., Robust optimization design of subway underframe based on dual response surface, Electr. Drive Locomot. (03), 120–124+131 (2019) [Google Scholar]
  15. M. Bidoki, M. Mortazavi, A new multidisciplinary robust design optimization framework for an autonomous underwater vehicle in system and tactic design, Proc. Inst. Mech. Eng. M 233, 918–936 (2019) [Google Scholar]
  16. B. Liu, Two typical assembly process analysis and robust design, Hunan University of Science and Technology, 2018 [Google Scholar]
  17. J. Wang, Research on deviation modeling and fixture robust design method of side wall assembly of high-speed train, Shanghai Jiao Tong University, 2012 [Google Scholar]
  18. K.G. Yu, Robust fixture design of compliant assembly process based on a support vector regression model, Int. J. Adv. Manufactur. Technol. 103, 111–126 (2019) [CrossRef] [Google Scholar]
  19. H. Quan, Y. Guo, Optimization design and experimental study of vortex pump based on orthogonal experiment, Sci. Progr. 103, 1–20 (2020) [Google Scholar]
  20. F.S. Ma, Six Sigma Management Statistical Guide (China Renmin University Press, Beijing, 2018) [Google Scholar]

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