No abstract

The proportion of electric vehicles in vehicle manufacturing is increasing, but with the endurance mileage of the battery improved, the collision safety problem of electric vehicles is becoming more prominent. To solve a multi-objective robust design problem with discrete variables for vehicle collision design, we propose the Grey-Taguchi robust optimization method. The Grey-Taguchi method transforms the multi-objective functions into a single grey relation grade sequence instead of the signal-noises ratio used in Taguchi and selects the optimal combination of design variables predicted by the minimum design of experimental and the analysis of means. For the vehicle crashworthiness problem, the Grey-Taguchi method can converge to the Pareto front with several iterations. The mass of frame, maximum lateral intrusion, and peak deceleration after the optimization are decreased 14.5 %, 32.3 %, and 17.8 %, respectively. The Grey-Taguchi robust optimization method can not only enhance the robustness of the optimization design but also improve the lightweight design performance and crashworthiness. The proposed method is considered promising for complex engineering design problems with multi-objectives and discrete design variables.

The artillery launch system directly influences the muzzle energy and launching accuracy, and therefore it is important to optimize the artillery launch system’s complete process to improve the launch performance. As the objective function of the artillery launch system is non-smooth with coupling parameters in sequential processes, conventional optimization methods are hard to converge for the muti-sequential process of the interaction between the projectile and the barrel. This paper develops a coupled dynamic model for artillery launching, which can predict the performance of the engraving process of the rotating band and the projectile motion in the barrel. The independent optimization problem of the artillery launch system is divided into two subspace problems, and a modified enhanced collaborative optimization (MECO) method with global search capability is proposed, in which the distance criterion and penalty design boundary method are implemented. Results show that the MECO is dedicated not only to satisfying compatibility between coupling parameters of the two sequential processes effectively but also to improving the projectile axial speed at the muzzle and launching accuracy. The MECO maintains a much stable level of convergence than the ECO when the original optimization problem is multimodal.