A new optimization strategy for multi-objective design of automotive seat frame

Optimal automobile seat design can play a significant role in passenger safety during high-speed accidents. To tackle the challenge of optimizing automotive seats, this paper presents a detailed optimization design method. In detail, the finite element models firstly established and validated through eight typical working conditions of automotive seats based on experimental data. Then, the optimized variables are screened and determined through contribution analysis, in which, the various perspectives are taken into account, such as the total cost and mass of seat materials, safety performance, and comfort index. Subsequently, an optimization strategy is constructed, which combines optimal Latin hypercube sampling, response surface method surrogate models, non-dominated sorting genetic algorithm-II, fuzzy analytic hierarchy process, entropy weighting method, gray relational analysis, and Visekriterijumsko KOmpromisno Rangiranje method for the optimal design of the automotive seat frame. Finally, a comprehensive comparative analysis of the optimal trade-off solution is carried out in terms of both decision methods and optimization strategies. The results show that ensuring the safety performance, the total cost, and total mass of the seat frame material decreased by 17.22% and 11.52%, respectively, as a result of the optimization strategy proposed in this paper, and the comfort performance is also improved to some extent. Therefore, the multi-objective optimization strategy proposed in this paper performs well in terms of both accuracy and effectiveness and provides a reliable reference for related multi-objective optimization.