Optimization of the oil cavity structure of the sliding shoe bearing for a grinding mill based on response surface and improved particle swarm algorithm

Abstract

Sliding shoe bearings are crucial components supporting the rotating cylinder in grinding mills. The structural parameters of oil cavities on their inner surface significantly affect both the load capacity and lubricating oil flow rate. To address the issues of excessive oil film load and oversupply of lubricating oil in a specific type of sliding shoe bearing, a simulation model of the oil film for the hollow shaft was developed. This study investigates the effects of key oil cavity parameters—sealing land width, throttling land width between main and auxiliary cavities, and their area ratio—on bearing performance. After validating the model via hydrostatic tests on a single sliding shoe, response surface methodology (RSM) and an improved multi-objective particle swarm optimization (MOPSO) algorithm were applied to optimize the oil cavity structure. Results demonstrate that both individual and coupled parameters considerably influence oil film load characteristics. Post-optimization, the bearing’s load capacity was reduced by 3.4% while still meeting mill requirements, and the oil supply flow rate was decreased by 17.3%, leading to improved efficiency and operational safety.