湖南工业大学学报2024，Vol.38Issue(1)：32-39,8.DOI:10.3969/j.issn.1673-9833.2024.01.005
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
摘要
Abstract
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.关键词
深沟球轴承/服役性能/粒子群-遗传混合算法/优化设计/应力分析Key words
deep groove ball bearing/service performance/particle swarm-genetic hybrid algorithm/optimized design/stress analysis分类
机械制造引用本文复制引用
叶帅,余江鸿,姚齐水,唐嘉昌,李睿..基于粒子群-遗传混合算法的深沟球轴承优化设计[J].湖南工业大学学报,2024,38(1):32-39,8.基金项目
湖南省自然科学基金资助项目(2021JJ50054,2022JJ50066) （2021JJ50054,2022JJ50066）
湖南省教育厅科研基金资助项目(21C0427) （21C0427）
