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基于熵权灰色关联分析的汽车车门多响应优化

Multi-Response Optimisation of Automotive Door Using Grey Relational Analysis with Entropy Weights.

作者信息

Chen Hao, Lu Chihua, Liu Zhien, Shen Cunrui, Sun Menglei

机构信息

Hubei Key Laboratory of Advanced Technology for Automotive Components, Wuhan University of Technology, Wuhan 430070, China.

Foshan Xianhu Laboratory of the Advanced Energy Science and Technology Guangdong Laboratory, Xianhu Hydrogen Valley, Foshan 528200, China.

出版信息

Materials (Basel). 2022 Aug 3;15(15):5339. doi: 10.3390/ma15155339.

Abstract

Tail-welded blanks (TWBs) are widely used in automotive bodies to improve the structural performance and reduce weight. The stiffness and modal lightweight design optimisation of TWBs for automotive doors was performed in this study. The finite element model was validated through physical experiments. An L27 (3) Taguchi orthogonal array was used to collect the sample points. The multi-objective optimisation problem was transformed into a single-objective optimisation problem based on the grey relational degree. The optimal combination of structural design parameters was obtained for a tail-welded door using the proposed method, and the weight of the door structure was reduced by 2.83 kg. The proposed optimisation method has fewer iterations and a lower computational cost, enabling the design of lightweight TWBs.

摘要

拼焊板广泛应用于汽车车身,以提高结构性能并减轻重量。本研究对汽车车门拼焊板的刚度和模态轻量化设计进行了优化。通过物理实验对有限元模型进行了验证。采用L27(3)田口正交表来采集样本点。基于灰色关联度将多目标优化问题转化为单目标优化问题。利用所提方法得到了拼焊车门结构设计参数的最优组合,车门结构重量减轻了2.83kg。所提优化方法迭代次数少、计算成本低,可实现拼焊板的轻量化设计。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9e0d/9369873/da855e30d0b8/materials-15-05339-g001.jpg

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