Multi‐objective optimization of main bearing assembly structure based on improved NSGA‐II

Abstract

Abstract Multi‐objective optimization of the main bearing assembly structure entails a high computational cost; moreover, the experimental measurement of the main‐bearing‐hole out‐of‐roundness has the problem of poor accuracy and inadequate repeatability. To address these issues, an improved NSGA‐II algorithm based on the fixed‐sized candidate set adaptive random testing (FSCS‐ART) algorithm and an adaptive strategy is proposed, and a more accurate method for measuring out‐of‐roundness based on the compensation method is developed. Accordingly, a mathematical model and a parametric model are established for optimization. Finally, the optimal design scheme is obtained by solving with the improved NSGA‐II algorithm. The results show that the proposed out‐of‐roundness measurement method has a high accuracy, with <5% error. The improved NSGA‐II algorithm exhibits a better solution convergence compared with the original algorithm does. The optimized solution of the improved NSGA‐II algorithm has a higher fitness when the two algorithms evolve to the same generation in the late stage, and the distribution of the Pareto optimal of the improved algorithm solution is closer to the Pareto frontier. After optimization, the stresses at the danger areas of the engine block and bearing cap are reduced by 13.67% and 6.71%, respectively. The out‐of‐roundness of the main bearing hole is reduced by 14.13%, and the maximum contact pressure on the dangerous contact surface is reduced by 14.08%. This study makes a significant contribution to the design of internal combustion engines because it facilitates the development of high‐power diesel engines by optimizing the reliability of the main bearing assembly structure.

Keywords

• improved NSGA‐II
• main bearing assembly structure
• multi‐objective optimization
• out‐of‐roundness
• reliability analysis