Generative design of texture for sliding surface based on machine learning

Textured surface is of fundamental and practical importance in numerous emerging applications due to its beneficial effects on the tribological performance. In this work, a machine learning based universal generative design framework is proposed for surface texturing designing by combining specific convolutional neural network with improved Monte Carlo search. The optimal patterns of surface texture generated by machine learning are systematically studied under different conditions. Our results show that the machine generated wavy and chevron-like textures have the potential to dramatically improve the tribological performance of sliding surface with infinite design domain. Compared with the reported optimal texture, the friction coefficient of machine generated texture is reduced to 27.3∼49.7%, and the load carrying capacity is increased to 126.1∼144.4%.