Improvement of lubrication performance of sliding pairs with multi-depth groove textures based on genetic algorithm

Abstract

Abstract During the wear and tear process of bearings, the friction coefficient between the friction pairs can be effectively decreased by employing the suitable surface texture on the frition surface. In the study, the distribution and depth variation of the surface texture were used as variables, and the genetic algorithm was used for iterative optimization to obtain the optimal texture distribution and depth. The friction and wear performance of the rectangular texture bearing sliding blocks was optimized. The depth of the texture was represented by a 4-bit binary number, and different binary numbers were set to represent different texture depths. Finally, the genetic algorithm was used for continuous iteration and evolution to obtain the optimal texture combination. The study showed that, compared with the regular texture with a depth of 0.2 μm, the friction coefficient decreased by 15.0% under the optimal texture with a non-uniform depth. Simultaneously, compared with the regular 3 μm deep texture, texture with a optimized depth makes the friction coefficient decreased by 37.5%, and the minimum oil film thickness increased by 0.979 μm. The optimal texture and oil film thickness combination obtained from the study can effectively reduce solid contact force and alleviate mechanical wear.