Citation

Wang D, Li S, Xie C. Int. J. Crashworthiness 2022; 27(4): 1193-1204.

Copyright

(Copyright © 2022, Informa - Taylor and Francis Group)

DOI

10.1080/13588265.2021.1926809

PMID

unavailable

Abstract

This study proposes a hybrid optimisation method integrating contribution analysis, Entropy weight Technique for Ordering Preferences by Similarity to Ideal Solution (E-TOPSIS), design of experiments (DOEs), and grey relational analysis (GRA) for optimisation design of front-end safety parts of automobile bodies to improve the crashworthiness and lightweight of vehicles. First, the finite element model (FEM) of vehicle is established, and the simulation analysis and test of frontal collision for vehicle are performed. The key crashworthiness quotas of vehicle and the mass of safety parts are extracted as the optimisation responses. Second, the material, wall thickness and cross-sectional shape of the safety parts of the automobile body are taken as initial design variables. A variable screening method of contribution analysis combined with E-TOPSIS is proposed. The comprehensive contribution coefficient of numerous initial design variables to the crashworthiness and lightweight of vehicle is calculated using the proposed method, and the design variables are objectively screened by taking this coefficient as an evaluation index. Third, the DOE using Hammersley sampling is performed. Finally, the GRA method is proposed to analyse the experimental design results and obtained the optimal parameter combination of the material, wall thickness, and cross-sectional shape of the safety parts. After optimisation of the front-end safety parts of the automobile body, the crashworthiness safety performance of the vehicle is significantly improved and the mass of the safety parts is reduced by 11.02%. Therefore, the proposed hybrid optimisation method can effectively improve the crashworthiness and lightweight of vehicle.

Language: en

Keywords

automobile body; contribution analysis; Crashworthiness optimisation; E-TOPSIS; grey relational analysis; lightweight