Optimization of Running-in surface morphology parameters based on Auto-ML model

Running-in is an important and relatively complicated process. Surface morphology prior to run-ning-in affects the surface morphology following the running-in process, which in turn influences friction and wear characteristics of the workpiece. Therefore, it is important to establish a running-in model based on surface morphology to investigate the process and optimize the surface design. According to the existing research, the application of machine learning in running-in wear modeling is not sufficiently investigated. The black-box model has a robust complex object simulation per-formance. In this paper, five machine learning methods are applied for running-in modeling per-formance based on surface morphology parameters. It is observed that the support vector machine method has the best performance. Then, the support vector machine model is carried out and tested. Finally, the change law of surface morphology parameters is obtained and optimization direction of surface morphology design is proposed. When the surface is required to have good oil storage per-formance, the root mean square deviation (Sq), the root mean square slope (Sdq), and the height of surface core roughness (Sk) should be increased, and the height difference of surface morphology (Sdc) and the surface area ratio (Sdr) should be moderately high. When surface improved surface supporting performance is desired, larger values of parameters the height difference of surface morphology (Sdc), the root mean square slope (Sdq), and the surface area ratio (Sdr) should be used.