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• W3126175510 abstract "Structural lightweight optimization is one of the key themes to continuously improve vehicle performance. However, most of the studies so far have not considered reliability and manufacturing issues brought about by the uncertainty of tolerances in structural lightweight optimization. In this study, a reliability-based design for lightweight vehicle structures with uncertain manufacturing accuracy is proposed. This method can provide not only the optimal design variables but also the maximum allowable deviation range. The main design variables lacking sample information are regarded as interval variables and other design parameters as probabilistic values. A progressive reliability-based design with uncertain manufacturing accuracy is established for lightweight vehicle structures by employing the transformation method of the interval model and the decoupling strategy of the probabilistic model. Firstly, the general test function is used to analyze the accuracy and efficiency of the reliability decoupling method. Then, lightweight optimization models of a classic cantilever beam and two vehicle structures (parking robot frame, energy absorption box) are established respectively, and the effectiveness of the present optimization method is verified. The optimized design results show that the optimal variables and the corresponding maximum allowable deviation range under acceptable manufacturing requirements can be obtained. The requirements for manufacturing accuracy can be reduced by appropriately increasing the deviation range of the design variables, which may lead to a reduction in manufacturing cost. This study aims to provide feasible design options by optimizing lightweight structures considering uncertain manufacturing accuracy." @default.
• W3126175510 created "2021-02-15" @default.
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• W3126175510 date "2021-07-01" @default.
• W3126175510 modified "2023-10-10" @default.
• W3126175510 title "Reliability-based design for lightweight vehicle structures with uncertain manufacturing accuracy" @default.
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• W3126175510 doi "https://doi.org/10.1016/j.apm.2021.01.047" @default.
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