首页|基于RSM和MOPSO的轴承沟道磨削工艺参数优化

基于RSM和MOPSO的轴承沟道磨削工艺参数优化

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  • 国家科技期刊平台
  • NETL
  • NSTL
  • 万方数据
探究角接触球轴承内圈沟道精磨工艺参数与加工质量的响应关系，确定最优工艺参数组，提高沟道加工质量。采用响应面法(RSM)与多目标粒子群优化(MOPSO)算法优化影响沟道加工质量的磨削深度、砂轮线速度和工件转速。首先利用RSM建立以沟道表面粗糙度和圆度误差为响应的显著不失拟模型;然后通过方差分析和响应曲面图研究工艺参数对响应的交互影响规律;最后采用MOPSO算法对模型进行多目标优化，利用K-means聚类法求解最优解集的折衷解，并进行试验验证。结果表明，磨削深度和砂轮线速度对沟道表面粗糙度和圆度误差影响极显著，工件转速对圆度误差的影响极显著，对表面粗糙度的影响显著;磨削深度与工件转速的交互作用对表面粗糙度影响显著，砂轮线速度与工件转速、磨削深度的交互作用对圆度误差影响显著。最优工艺参数组经试验验证，表面粗糙度和圆度误差较优化前分别减小了8。14％和16。03％。基于RSM和MOPSO算法结合的回归模型整体和单个变量显著，且有较高的预测精度，寻优后的工艺参数组可获得良好的优化效果。
Optimization of bearing raceway grinding process parameters based on RSM and MOPSO
The response relationship between process parameters and machining quality of inner raceway precision grinding of an-gular contact ball bearings was explored,and the optimal process parameter set to improve raceway machining quality was identit-ying.Response Surface Methodology(RSM)and Multi-Objective Particle Swarm Optimization(MOPSO)algorithm were used to optimize the grinding depth,grinding wheel speed,and workpiece speed that affect raceway machining quality.First,the RSM was used to establish a significant non-degenerate model with the surface roughness and roundness error as responses.Then,vari-ance analysis and surface plots were used to study the interactive effects of process parameters on the responses.Finally,the MOPSO algorithm was applied for optimizing the model,and K-means clustering method was used to solve the compromised solu-tion of the optimal solution set,and experimental validation was conducted.The results showed that grinding depth and grinding wheel linear speed had highly significant effect on the surface roughness and roundness error of the raceways,and workpiece speed had highly significant effect on the roundness error and a significant effect on the surface roughness.The interaction be-tween grinding depth and workpiece speed had a significant effect on the surface roughness,and the interaction of grinding wheel linear speed with workpiece speed and grinding depth had a significant effect on the roundness error.The optimized parameter set was verified by experiments,and the surface roughness and roundness error were reduced by 8.14％and 16.03％,respectively,compared to preoptimization.The regression model based on RSM and MOPSO algorithm is significant for the whole and individu-al variables,and has high prediction accuracy,and the optimized process parameter set can obtain good optimization results.

angular contact ball bearingResponse Surface Methodology(RSM)Multi-Objective Particle Swarm Optimization(MOPSO)algorithmraceway grindingparameter optimization

蒋心想、李成、时建纬、陈栋

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郑州大学机械与动力工程学院,郑州 450001

角接触球轴承 响应面法 多目标粒子群优化算法 沟道磨削 参数优化

国家自然科学基金项目河南省水下智能装备重点实验室开放基金项目

12302106ZT22064U

2024

10.16731/j.cnki.1671-3133.2024.07.013

现代制造工程
北京机械工程学会 北京市机械工业局技术开发研究所

现代制造工程

CSTPCD北大核心
影响因子：0.374
ISSN：1671-3133
年,卷(期)：2024.(7)
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