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A Bayesian-reliability based multi-objective optimization for tolerance design of mechanical assemblies

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  • Ghaderi, A.
  • Hassani, H.
  • Khodaygan, S.

Abstract

Tolerances significantly affect the assemblability of components, the product's performance, and manufacturing cost in mechanical assemblies. Despite the importance of product reliability assessment, the reliability-based tolerance design of mechanical assemblies has not been previously considered in the literature. In this paper, a novel method based on Bayesian modeling is proposed for the tolerance-reliability analysis and allocation of complex assemblies where the explicit assembly functions are difficult or impossible to extract. To reach this aim, a Bayesian model is developed for tolerance-reliability analysis. Then, a multi-objective optimization formulation is proposed for obtaining the optimum tolerances of components to minimize cost and maximize product performance. Subsequently, Non-dominated Sorting Genetic Algorithm II (NSGA-II) is employed for solving multi-objective optimization. Then, the enhanced TOPSIS is used to find the best optimum tolerances from the optimum Pareto solutions. Using the importance vector concept, a sensitivity analysis approach is used to determine the effects of design variables on the product reliability level and improve assembly reliability to the desired level. Finally, to exhibit the applicability of the proposed method, a transmission planetary gear system is considered, and the obtained results are compared and discussed for verification.

Suggested Citation

  • Ghaderi, A. & Hassani, H. & Khodaygan, S., 2021. "A Bayesian-reliability based multi-objective optimization for tolerance design of mechanical assemblies," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    • Handle: RePEc:eee:reensy:v:213:y:2021:i:c:s0951832021002787
    DOI: 10.1016/j.ress.2021.107748
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    References listed on IDEAS

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    1. Alban, Andres & Darji, Hardik A. & Imamura, Atsuki & Nakayama, Marvin K., 2017. "Efficient Monte Carlo methods for estimating failure probabilities," Reliability Engineering and System Safety, Elsevier, vol. 165(C), pages 376-394.
    2. Ziegler, Philipp & Wartzack, Sandro, 2015. "Sensitivity Analysis of features in tolerancing based on constraint function level sets," Reliability Engineering and System Safety, Elsevier, vol. 134(C), pages 324-333.
    3. Liu, Xintian & Mao, Kui & Wang, Xiaolan & Wang, Xu & Wang, Yansong, 2020. "A modified quality loss model of service life prediction for products via wear regularity," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    4. Wilma Polini & Andrea Corrado, 2019. "A Geometric Model for Tolerance Analysis with Manufacturing Signature and Operating Conditions," International Journal of Manufacturing, Materials, and Mechanical Engineering (IJMMME), IGI Global, vol. 9(3), pages 1-13, July.
    5. Cheng, Kai & Lu, Zhenzhou, 2021. "Adaptive Bayesian support vector regression model for structural reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 206(C).
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    Cited by:

    1. Qi, Yaqun & Jin, Ping & Cai, Guobiao & Li, Ruizhi, 2022. "A Bi-stage Multi-objective Reliability-based Design Optimization Using Surrogate Model for Reusable Thrust Chambers," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    2. Wang, Guodong & Shao, Mengying & Lv, Shanshan & Kong, Xiangfen & He, Zhen & Vining, Geoff, 2022. "Process parameter optimization for lifetime improvement experiments considering warranty and customer satisfaction," Reliability Engineering and System Safety, Elsevier, vol. 221(C).

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