Multi-Objective Optimization of Automotive Front Rail Based on Surrogate Model and NSGA-II

With the rapid development of automotive industry, more and more attention has been paid to the lightweight and safety design. Crashworthiness optimization is an essential part in automotive design. In this study, a non-dominated sorting genetic algorithm II (NSGA-II) based on Kriging model is proposed to optimize the structure of automotive frontal rail to meet the requirements of crashworthiness and lightweight. The material of frontal rail is mild steel, which performs well in strength. Kriging surrogate model is employed to replace traditional finite element model, which will reduce much computational time and improve the efficiency. Then NSGA-II is applied to solve the multi-objective optimization problem. The results illustrate that the Pareto optimal front obtained by NSGA-II exhibits good performance on convergence and diversity. And then the optimal scheme for design is selected, the accuracy is proved to be high. Compared to baseline model, the optimized automotive frontal rail shows significant improvement on crashworthiness and achieves 7.5% weight reduction.