基于多目标粒子群-遗传混合算法的高速球轴承优化设计方法  

High-speed ball bearing optimal design method based on multi-objective particle swarm optimization and genetic algorithm

作　　者：杨文[1,2] 叶帅 姚齐水 余江鸿 胡美娟[1,2] YANG Wen;YE Shuai;YAO Qishui;YU Jianghong;HU Meijuan(School of Intelligent Manufacturing,Hunan Railway Professional Technology College,Zhuzhou 412001,China;Key Laboratory of High Performance Rolling Bearing Technology in Hunan Province,Zhuzhou 412007,China;School of Mechanical Engineering,Hunan University of Technology,Zhuzhou 412007,China;Zhuzhou Lince Group Holding Co.,Ltd.,Zhuzhou 412001,China)

机构地区：[1]湖南铁道职业技术学院智能制造学院,湖南株洲412001 [2]高性能滚动轴承技术湖南省高校重点实验室,湖南株洲412007 [3]湖南工业大学机械工程学院,湖南株洲412007 [4]株洲联诚集团控股股份有限公司,湖南株洲412001

出　　处：《机电工程》2025年第2期226-236,共11页Journal of Mechanical & Electrical Engineering

基　　金：国家自然科学基金资助项目(51175168);湖南省自然科学基金资助项目(2021JJ60069);湖南省教育厅优秀青年项目(21B0897)。

摘　　要：目前以新能源汽车电驱系统等为代表的超高转速运行场景越来越多,对轴承类关键零部件的性能要求也不断提高,对轴承的承载性能和温升控制也提出了更高的要求。为了优化轴承的结构,提升其服役性能,以新能源汽车电驱系统6206轴承为例,提出了一种基于多目标粒子群-遗传混合算法的球轴承结构优化设计方法。首先,建立了以轴承最大额定动载荷、最大额定静载荷和最小摩擦生热率为目标函数的优化数学模型;然后,利用多目标粒子群算法(MOPSO)的全局搜索能力和改进非支配排序遗传算法(NSGA-II)的进化操作,引入粒子寻优速度控制策略、交叉变异策略和罚函数机制,解决了带约束优化问题求解和局部最优问题,增强了算法的收敛速度和解集探索能力;最后,在特定工况下对轴承结构进行了优化,采用层次分析法,从Pareto前沿中优选了内外圈沟曲率半径系数、滚动体数量、滚动体直径和节圆直径的最优值。研究结果表明:在16 kN径向载荷、15 000 r/min的高转速工况下,以新能源汽车电驱系统6206型深沟球轴承为例进行了分析,结果显示,优化后的轴承接触应力下降了21.2%,应变下降了25.6%,摩擦生热下降了16.7%,体现了该方法在收敛性能、寻优速度等方面的优势。该优化设计方法可为球轴承的工程应用提供有价值的参考。At present,there are more and more ultra-high speed operation scenes represented by electric drive systems of new energy vehicles,and the performance requirements of key parts of bearings are also constantly improved,and higher requirements are also put forward for the bearing capacity and temperature rise control.In order to optimize the bearing structure to enhance its service performance,taking 6206 bearing of electric drive system of new energy vehicle as an example,a multi-objective particle swarm-genetic hybrid algorithm-based optimal design method for ball bearing structure was proposed.Firstly,an optimization mathematical model was constructed with the objective of maximizing the dynamic load rating,static load rating and minimizing the friction heat generation rate of the bearings.Then,using the global search ability of multi-objective particle swarm optimization(MOPSO) and the evolutionary operation of improved non dominated sorting genetic algorithm(NSGA-II),the particle optimization speed control strategy,cross mutation strategy and penalty function mechanism were introduced to solve the constrained optimization problem and local optimization problem,and enhance the convergence speed and the ability to explore the set.Finally,the bearing structure was optimized under specific working conditions,and the analytic hierarchy process(AHP) was used to select the optimal values of the inner and outer ring groove curvature radius coefficient,the number of rolling elements,the diameter of rolling elements and the pitch diameter from the Pareto front.The research results show that a case study is conducted on the 6206 deep groove ball bearing of the electric drive system for new energy vehicles under high-speed conditions of 16 kN radial load and 15 000 r/min.The optimized bearing contact stress decreases by 21.2%,strain decreases by 25.6%,and frictional heat generation decreases by 16.7%,demonstrating the advantages of the proposed method in convergence performance and optimization speed.The optimization desig

关 键 词：高速球轴承结构设计 多目标粒子群-遗传混合算法 改进非支配排序遗传算法 优化设计目标函数 层次分析法 6206型深沟球轴承 

分 类 号：TH133.33[机械工程—机械制造及自动化] U469.7[机械工程—车辆工程]