Influence and optimization of python-scale-inspired surface textures on the friction and wear behavior of bearing steel

Bearing steel is a key material for thrust ball bearings, and surface wear is one of the primary causes of bearing failure. Such wear significantly shortens service life and compromises operational reliability, particularly under complex working conditions where the problem becomes more pronounced. To improve the surface performance of bearing steel, this study applies a bionic texturing approach. Numerical simulations and friction experiments are combined to investigate the friction and wear behavior of python-scale-inspired surface textures fabricated at different rotation angles on thrust bearing steel. Simulation results show that as the rotation angle of the textured pattern increases, the friction coefficient first decreases and then increases, while the load-carrying capacity exhibits the opposite trend. At α = 60°, the texture achieves both the lowest friction coefficient and the highest load-carrying capacity, with the maximum pressure difference reaching 4.76 Pa. Experimental results confirm that at α = 60°, the textured sample exhibits the lowest average friction coefficient (0.218) and the lowest wear rate (0.725 × 10−5 mm3/N m), representing reductions of 36.81% and 24.71%, respectively, compared with the untextured sample. Overall, textures with all rotation angles enhance the friction-reducing and wear-resisting performance of thrust bearing steel, with α = 60° providing the most significant improvement.