Crashworthiness design of an automotive S‑rail using ANN‑based optimization to enhance performance and safety

The automotive S-rail plays a crucial role in frontal car crashes, aiming to absorb impact energy and reduce passenger injury. This paper innovatively presents an optimization approach to determine the optimal configuration of an S-rail featuring a tapered, multicell front section. The structural design of the S-rail is conceptualized within the design space of a heavy quadricycle vehicle and subsequently subjected to numerical investigation through LS-DYNA using nonlinear explicit dynamics analyses. The objective is to maximize energy absorption while minimizing the S-rail's initial peak force (IPF) and mass. An artificial neural network (ANN) is employed to construct surrogate models for the optimization process. The non dominated sorting genetic algorithm, integrated with the ANN, yields an optimal S-rail design. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method, using the mean weighting method on the Pareto frontier optimal solution set, is appropriately applied to select the optimal solution. The optimized S-rail shows improved specific energy absorption compared to the baseline model while maintaining a low IPF. In conclusion, this study highlights the superior predictability of an ANN over conventional quadratic response surface methodology. The results confirm the effectiveness of the ANN-based optimization approach and the selection of a compromise solution using the TOPSIS technique. The proposed procedure has substantial potential to enhance the safety and performance of automotive S-rails.