基于粒子群-遗传混合算法的深沟球轴承优化设计

An Optimized Design of Deep Groove Ball Bearings Based on Particle Swarm-Genetic Hybrid Algorithm

为了提高深沟球轴承的服役性能,提出一种基于粒子群-遗传混合算法的优化设计方法。其以额定动载荷和额定静载荷为目标函数,以滚动体直径、节圆直径、滚动体数目和内外圈滚道沟曲率半径系数为设计变量,基于粒子群算法,引入罚函数和遗传交叉、变异操作,解决带约束优化问题求解和局部最优问题。并以6206型轴承为算例,对优化后的轴承进行应力分析和敏感度分析。结果表明,所提出算法的收敛性能较好、优化能力较强、运算速度较快,优化后的深沟球轴承接触应力下降了31.7%,从而验证了所提出方法的有效性。

In view of an improvement of the service performance of deep groove ball bearings,an optimization design method has thus been proposed based on particle swarm-genetic hybrid algorithm.With rated dynamic load and rated static load as the objective function,and with the diameter of rolling elements,pitch circle diameter,number of rolling elements,and curvature radius coefficient of inner and outer raceways as the design variables,based on the particle swarm optimization,penalty functions and genetic crossover and mutation operations are introduced for the solution of constrained optimization problems and local optimization problems.Taking 6206 bearing as a calculation example,a stress and sensitivity analysis is carried out for the optimized bearing.The results show that the proposed algorithm is characterized with an improved convergence performance,a stronger optimization ability,and a faster computational speed.The optimized deep groove ball bearing contact stress has decreased by 31.7%,thus verifying the validity of the proposed method.