LAUSR

This paper proposes a hybrid method combining the Contribution Analysis Method, the Radial Basis Function Neutral Network (RBFNN)-Response Surface Method (RSM) hybrid surrogate modeling method, the Multi-Objective Particle Swarm Optimization (MOPSO) algorithm and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), used for structure-material integrated multi-objective lightweight design of the front end structure of an automobile body. First, Contribution Analysis Method provides an effective approach to determine the final parts for lightweight design, and fourteen thickness variables and thirteen material variables are finally selected. Second, RBFNN-RSM hybrid surrogate modeling method successfully constructs the mapping between the input thickness-material variables and the output lightweight controlling quotas of the automobile body. Third, the MOPSO solves the multi-objective lightweight design process, considering the total mass and the torsional stiffness of the automobile body, the maximum impact acceleration at lower end of the B-pillar and the total material cost of the selected design parts as four conflicting objective functions. Accordingly, a set of Pareto-optimal solutions are obtained. Finally, a decision-making procedure based on TOPSIS method ranks all these Pareto-optimal solutions from the best to the worst for determining the best compromise solution. In addition, the proposed lightweight design method is demonstrated by the comparison among the baseline design, the actual experiment and the optimal design. The results show that the automobile body is lightweight designed with a mass reduction of 4.12 kg while other mechanical performance are well guaranteed. Hence, the proposed lightweight design method could be well applied to the lightweight design of the automobile body.