Lightweight optimization design for automotive front seat frame based on comprehensive Sobol global sensitivity analysis

The screening of high-dimensional design variables has garnered significant attention in engineering optimization. However, the challenge of screening design variables while comprehensively considering multi-objective attributes remains unresolved in the multi-objective optimization of automotive seat structures. To address this issue, this study proposes a Comprehensive Sobol Global Sensitivity Analysis (CSGSA) method. Traditional multi-criteria decision-making methods often involve complex weight calculations, further complicating multi-attribute decision-making processes. The proposed CSGSA method integrates the strengths of Sobol global sensitivity analysis and Modified Preference Selection Index (MPSI), eliminating the need for weight calculations and effectively addressing the design variable screening challenge in the multi-objective optimization of automotive seat structures. Based on the aforementioned methods, this paper applies the CSGSA and MPSI methods to the lightweight optimization of automotive seat frame. Firstly, two high-precision finite element simulation models, experimentally validated for modal analysis, are constructed, and the CSGSA method is employed to screen design variables for lightweight optimization. Secondly, several advanced surrogate models are constructed using optimal Latin hypercube sampling, with the third-order Response Surface Model (RSM3) identified as the optimal surrogate model. Furthermore, the Multi-Objective Global Optimization (MOGO) technique, RSM3-Non-dominated sorting genetic algorithm-II, is employed to obtain the Pareto Frontier Solutions (PFSs) for this optimization design. The best compromise solution is then determined from the PFSs using the MPSI method. The optimized automotive seat significantly increased the first-order modal frequency of the entire seat and seat frame by 18.83% and 27.08%, respectively, concurrently achieving a weight reduction of 4.038%. Finally, the optimized automotive seat is subjected to a whiplash test. The test results show high scores, indicating that the seat provides effective protection for the driver and passengers in the event of a collision. Additionally, the optimization results of the proposed MOGO technique are compared with those of the classical multi-objective local optimization technique, highlighting the superior optimization performance of MOGO.