基于参数敏度分析的轮毂轴承寿命多目标优化

Multi-objective Optimization of Hub Bearing Life Based on Parameter Sensitivity Analysis

为提高某型第三代轮毂轴承受载疲劳寿命,以额定动载荷和额定静载荷作为响应指标,采用正交试验设计对轮毂轴承结构参数进行灵敏度分析,以轮毂轴承滚珠数量、滚珠直径、节圆直径及内外沟曲率半径等主要影响因素作为设计变量,建立轮毂轴承疲劳寿命多目标优化数学模型。分别采用非劣排序遗传算法(NSGA-II)、多岛遗传算法(MIGA)和最优化粒子群优化算法(MOPSO)进行优化求解,得到了轮毂轴承最优结构设计参数,采用有限元仿真分析方法对优化前后的旋压铆合装配成形轮毂轴承疲劳寿命进行对比验证。结果表明:上述优化策略实现了由2个目标函数主导的轮毂轴承整体性能提升,改善了轮毂轴承各组件的应力集中情况,提高了轮毂轴承的疲劳寿命,优化后结构最大应力较初始设计降低了约5.1%,寿命较初始设计结构增加了约3.8%,表明轮毂轴承的疲劳寿命多目标优化设计是有效的。

In order to improve the loading fatigue life of a certain type of third-generation hub bearing,with rated dynamic load and rated static load as the response indexes,the sensitivity analysis of the structural parameters of hub bearing was carried out by orthogonal experimental design.The main influencing factors such as the number of rollers,the diameter of rollers,the diameter of pitch circle and the curvature radius of inner and outer grooves of the hub bearing were taken as design variables,and the multi-objective optimization mathematical model of the fatigue life of the hub bearing was established.The non-dominated sorting genetic algorithm(NSGA-II),multi-island genetic algorithm(MIGA)and optimization particle swarm optimization algorithm(MOPSO)were used to optimize the solution,and the optimal structural design parameters of hub bearing were obtained.The finite element simulation analysis method was used to compare and verify the fatigue life of the spinning and riveting assembly forming hub bearing before and after the optimization.The results show that the above optimization strategy has achieved the improvement of the overall performance of the hub bearing dominated by two objective functions,improved the stress concentration of each component of the hub bearing,and increased the fatigue life of the hub bearing.After optimization,the maximum stress of the structure is reduced by about 5.1%compared with that of the initial design and the life is increased by about 3.8% compared with that of the initial design structure, which indicates that the multi-objective optimization design of the fatigue life of the hub bearing is effective.