Computational modeling and multi-objective optimization of a thin-walled steel crash box for enhanced crashworthiness and failure mitigation

Abstract

Thin-walled crash boxes are key energy-absorbing components whose geometry influences vehicle mass, impact-force transmission, and crash energy dissipation. Existing optimization approaches often rely on complex geometries or assess structural failure only indirectly, which limits their application to locally manufacturable steel components. In this study, an integrated framework that couples an ABAQUS/Explicit model with the non-dominated sorting genetic algorithm II (NSGA-II) is developed for a square steel crash box that can be produced with conventional manufacturing processes. Wall thickness, box length, trigger-hole diameter, and trigger-hole position were optimized to minimize mass and peak crushing force while maximizing specific energy absorption (SEA), subject to equivalent plastic-strain and connection-safety constraints. The baseline design had a mass of 4.25 kg, an SEA of 9.34 kJ/kg, and a peak force of 115.2 KN. The selected balanced Pareto-optimal design reduced the mass to 3.98 kg (6.4%), increased the SEA to 10.15 kJ/kg (8.7%), and lowered the peak force to 108.4 KN (5.9%). Its connection safety factor increased to 1.18, while the maximum equivalent plastic strain remained below the allowable limit of 0.35. Sensitivity analysis identified wall thickness and trigger-hole geometry as the dominant design variables. The proposed framework provides a practical route to improving crashworthiness using conventional steel and accessible manufacturing processes.