Volume 48 Issue 9
Sep.  2022
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Article Contents
KANG Rui, LIU Haoran, ZHANG Qingyuan, et al. Function oriented belief reliability design and optimization of new torsion spring electrical connectors[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48(9): 1745-1756. doi: 10.13700/j.bh.1001-5965.2022.0323(in Chinese)
Citation: KANG Rui, LIU Haoran, ZHANG Qingyuan, et al. Function oriented belief reliability design and optimization of new torsion spring electrical connectors[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48(9): 1745-1756. doi: 10.13700/j.bh.1001-5965.2022.0323(in Chinese)

Function oriented belief reliability design and optimization of new torsion spring electrical connectors

doi: 10.13700/j.bh.1001-5965.2022.0323
Funds:

National Natural Science Foundation of China 62073009

Fund for Science & Technology on Reliability & Environmental Engineering Laboratory 6142004210401

More Information
  • Corresponding author: ZHANG Qingyuan, E-mail: zhangqingyuan@buaa.edu.cn
  • Received Date: 06 May 2022
  • Accepted Date: 27 May 2022
  • Publish Date: 14 Jun 2022
  • To evaluate reliability of new products, function-oriented design should comprehensively consider the influence of new structure and technology, multiple design variables, and complex working environments, and reasonably quantify uncertainty effects. This will ensure high reliability from the original design. Based on the belief reliability theory, this study investigates a performance margin-based and function-oriented method for belief reliability design and optimization. The basic procedure of the function-oriented reliability design is proposed for the development of new torsion spring electrical connectors, which includes four steps, i.e., initial value selection of design variables, performance margin modeling, uncertainty quantification, and reliability analysis and optimization. This procedure is exemplified in the design and development of a new type of torsion spring electrical connector. The optimal design of multidimensional discrete-continuous variables under the influence of their uncertainties is realized based on orthogonal experiments, response surface modeling, and simulated annealing heuristic optimization.

     

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