摘要：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.
关键词：Automobile body frame;multi-objective optimization;weight;rigidity;frequency;artificial neural network;genetic algorithm
