A multi-objective surrogate-based optimization of the crashworthiness of a hybrid impact absorber

This paper applies surrogate-based multi-objective optimization techniques to a crashworthiness problem in which the impact performance of a frontal crash absorber made of steel and a glass-fiber reinforced polyamide is optimized. Two well known crashworthiness indicators are considered as contrasting objective functions: the Specific Energy Absorption (SEA) and the Load Ratio (LR), whose responses are approximated by multiple types of surrogate models due to their computational cost and their noise levels. These models are quadratic and cubic polynomials, Gaussian process (kriging) and multivariate adaptive regression splines (MARS). The finite element model includes strain-rate sensitive properties, which is verified with experimental data from a drop-tower test. The thickness of the different parts, the geometry of the cross-section and the offsets of the reinforcement parts are chosen as design variables. Pareto solution is obtained after both models are verified. Results show improvements in both functions by almost 50% compared to the original design. •We have carried out a multi-objective optimization of a frontal crash absorber.•The absorber consists of an original design with GFRP and steel parts.•A reliable solution was obtained by using several surrogate models.