A novel approach to the co-optimized design of lightweight and crashworthiness for CFRP automotive front crash beam

Complex engineering multi-objective optimization problems that utilize high-fidelity simulation models often encounter challenges related to computational expense; however, surrogate-based optimization methods can effectively mitigate this limitation. This paper presents an integrated optimization framework that encompasses optimal Latin hypercube experimental design, a hybrid surrogate model combining a Pointer optimization algorithm with weighted prediction error reduction, a non-dominated sorting genetic algorithm-II, and the technique for order preference by similarity to ideal solutions (TOPSIS), which is based on grey relational analysis and the entropy weighting method as integral components of a comprehensive systematic optimization strategy. Subsequently, the feasibility of this strategy is validated through a case study centered on automotive seat engineering optimization, underscoring the benefits of the proposed hybrid surrogate model approach combined with the enhanced TOPSIS methodology. Thereafter, the thickness and number of layers of the carbon fiber-reinforced polymer (CFRP) automotive front bumper crossmember are established according to the principle of equal stiffness replacement. Ultimately, this optimization strategy is implemented in a multi-objective design concerning the layup sequence of the CFRP crash beam. The findings of this study indicate that the optimized CFRP crash beam demonstrates superior crashworthiness compared to the original steel crash beam, while achieving a significant reduction in overall weight. The optimized CFRP bumper demonstrates superior crash performance compared to the original steel structure, reducing the maximum crash force by 7.25%, while maintaining stable intrusion and energy absorption. Additionally, the optimized bumper achieves a substantial 67.5% weight reduction.