Abstract: In order to improve the performance of angular contact ball bearings, a multi-objective optimization model of double-row angular contact ball bearing structure was established with the rated dynamic load and rated static load of double-row angular contact ball bearings as the optimization target. Aiming at the defects that ordinary particle swarm algorithm was easy to fall into local optimum and couldn’t effectively solve the optimization problem with constraints, a penalty term was added into the objective function to transform the optimization problem with constraints into an unconstrained optimization problem, which was based on the standard particle swarm optimization algorithm. Taking the 3210 angular contact ball bearing as an example, the proposed penalty function particle swarm optimization algorithm was used to optimize the design. After optimization, the rated value of dynamic load and static load of the bearing were increased by 66.46% and 70.60% respectively. The results show that the performance of angular contact ball bearing can be improved by using penalty function particle swarm optimization algorithm.

Key words: vehicle engineering; angular contact bearing; optimization design; particle swarm optimization; multi-objective optimization

摘要： 为了提高角接触球轴承的性能，以双列角接触球轴承的额定动载荷和额定静载荷最大为优化目标建立了双列角接触球轴承结构的多目标优化模型。针对普通粒子群算法易于陷入局部最优的缺陷和不能有效解决带约束条件的优化问题，基于标准粒子群算法，在目标函数中加入惩罚项，将带约束条件的优化问题转变成无约束优化问题。最后以3210角接触球轴承为算例，通过提出的罚函数粒子群算法对其进行了优化设计，经优化后，轴承动、静载荷额定值各自提高了66.46%和70.60%。结果表明，采用罚函数粒子群算法能较好的提高角接触球轴承的性能。

关键词: 车辆工程；角接触轴承； 优化设计； 粒子群算法； 多目标优化