Evaluation and optimization of axial piston pump textured slipper bearings with spherical dimples based on hybrid genetic algorithm

Abstract

The spherical dimple texture have been designed on the rough surface of slipper bearing for improving the lubrication performance in axial piston pump. In this work, we have investigated and optimized the structure parameters of textures to obtain minimum friction coefficient as well as maximum loading capacity. Optimization of the geometry parameters of dimple texture by the integration of a hybrid evolutionary optimization method based on the sequential quadratic programming and genetic algorithm. Parametric analysis is applied for the evaluation of the impact level of geometry parameters on lubrication performance. The results shows that hybrid genetic method can be used for the optimization of slipper bearing with spherical dimple textures to generate lower friction coefficient and greater capacity of load carrying. The carrying capacity and friction coefficient of slipper bearing demonstrate a 64.8% and 4.5% improvements after multi-objective optimization. When the texture radius and depth are set to 18 µm and 0.8 µm, there exists the greatest load carrying force and lowest friction coefficient. This work presents a key designing guide for axial piston pump textured slipper bearings.