Collaborative optimization design of lightweight and crashworthiness of the front-end structures of automobile body using HW–GRA for Pareto mining

Abstract

To collaboratively improve the lightweight and frontal collision safety performance of the automobile body, the material–structure–performance integrated multi-objective optimization design of the front-end structure was implemented in this study. The hybrid weight–grey relational analysis (HW–GRA) method was proposed to address the problem that the Pareto optimal compromise solution of multi-objective optimization cannot be easily selected. The design variables of the material and structural parameters were recorded through material type coding and model parameterization. To satisfy the basic static–dynamic performance constraints of the body, the RSM–Kriging surrogate model, design of experiments, and multi-objective particle swarm optimization algorithm were used to optimize the material and structural parameters to improve the lightweight and crashworthiness of the body. The Pareto optimal compromise solution was determined using the HW–GRA method. The decision result of the proposed method was more robust and rational than those of single weighting techniques combined with GRA and hybrid weighting approach combined with TOPSIS. After the multi-objective optimization and decision-making, the basic static–dynamic performance of the body demonstrated a change of less than 2.0%, which satisfied the constraint requirements. The mass of the body was reduced by 2.4 kg, and the frontal collision safety performance of the body was significantly improved.