Multi-objective optimization of automobile body frame considering weight, rigidity, and frequency for conceptual design
Xiaoguang Wang, Pengfei Sun, Wenjie Zuo, Jiantao Bai
Abstract
At the conceptual stage of product design, simplified automobile body frame constituted by thin-walled beams can effectively be used to predict global performances, including weight, rigidity, and frequency. These performances can be improved by optimizing their cross-sectional shapes (CSS) of thin-walled beams. However, it is difficult to optimize the CSS while satisfying multiple performances, because this is a multiple objectives and design variables optimization problem. The gradient-based optimization algorithms are difficult to obtain the global optimal solutions for the automobile structures. Therefore, this paper proposes an innovative multi-objective optimization method to design the CSS of automobile body by using the non-dominated sorting genetic algorithm (NSGA-II) combining with the artificial neural network. Firstly, the mechanical properties of the CSS are summarized, including open-cell, single-cell, and double-cells. These mechanical properties determine the performances of the automobile structure. Then, the multi-objective optimization model is created by using the NSGA-II while considering the weight, stiffness, and frequency, which is implemented in the self-developed CarFrame software. Finally, the proposed method is verified by optimizing the CSS for the A-pillar of automobile frame.