Frontal crashworthiness optimization for a light-duty vehicle based on a multi-objective reliability method

In the current research, the main method to improve passenger safety in collision accidents and achieve lightweight of body-in-white (BIW) is deterministic optimization. Due to the perturbations of the design variables, the deterministic optimization results will be invalid. To solve this problem, a multi-objective reliability method based on integrating surrogate model and Six-Sigma criteria is proposed. As an example, a finite element model of a light-duty vehicle has been developed and validated by full-scale crash test. Through analyzing the overall energy absorption performance during frontal crash simulation, six main parts have screened, and their thickness values have been selected as design variables. Increasing energy absorption and reducing the weight of the structure were selected as design objectives. The front wall invasion (D), the peak acceleration (A) and the energy difference Iota Delta E Iota absorbed by the left and right side stringer are as constrains. Take the design variables obey normal distribution, and use a genetic algorithm method to generate Pareto solutions. The Pareto solutions of different iteration numbers with a sigma level of 4.5 are analyzed, and the results show that both deterministic optimization and reliability optimization can increase energy absorption. While deterministic optimization increases total energy absorption by 7.6%, but the total weight is increased by 6.9%. And the reliability of the two constraint functions front wall invasion and peak acceleration are lower than 60%. The reliability optimization decreases the weight by 5.3% and increases the energy absorption by 11.7%. Meanwhile, the value of constraint functions are lower, which is beneficial to the crashworthiness of the vehicle, and the reliability of them reaches more than 99%.