Design Exploration and Optimization of a Multi-Corner Crash Box under Axial Loading via Gaussian Process Regression

Crash box is the most commonly used energy-absorbing systems for increasing transportation safety. Based on this system, design optimization of crash box is important to meeting safety standards with high performance. Therefore, this study aimed to propose a crucial crash box design featuring a 20-corner structure. This new design was expected to increase the number of folds and reduce the size upon impact, which can further elevate specific energy absorption (SEA). In addition, the study implemented a Gaussian Process Regression (GPR) surrogate model for the first time to optimize the multi-corner crash box design. This method reduced the computational cost of the design optimization process while effectively handling complex configurations. The optimization also focused on dimensional parameters such as thickness and perimeter. An explicit nonlinear finite element method was used for axial loading analysis to assess the crash impact performance. The computational results showed good agreement with experimental data where the performance of the 20-corner structure design was compared with other shapes including square, circular, and 12-corner designs. The results showed that the 20-corner structure design provided the most optimum SEA and Crushing Force Efficiency (CFE). Furthermore, the optimized 20-corner design via GPR-model led to a further performance increment with an increase of 8-9% higher SEA and CFE. The following main conclusions can be drawn (i) the cross-sectional geometry significantly influenced crashworthiness performance with the MC20 configuration achieving the best Pm, CFE, and SEA values compared to other configurations. Therefore, the study concluded that the MC20 configuration was the most efficient among the other configurations; (ii) GPR proved advantageous in the optimization process, offering flexibility for complex nonlinear functions, reducing computational costs, and providing error predictions to further refine the optimization model; and (iii) optimization of the MC20 configuration using the GPR surrogate model focused on a single objective which was to maximizing Pm.