Abstract: The response relationship between process parameters and machining quality of inner raceway precision grinding of angular contact ball bearings was explored, and the optimal process parameter set to improve raceway machining quality was identitying. 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, variance 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 solution 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 between 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 individual variables, and has high prediction accuracy, and the optimized process parameter set can obtain good optimization results.

Key words: angular contact ball bearing, Response Surface Methodology (RSM), Multi-Objective Particle Swarm Optimization (MOPSO) algorithm, raceway grinding, parameter optimization