Optimization of running-in surface morphology parameters based on the AutoML model.

ABSTRACT: Running-in is an important and relatively complicated process. The surface morphology prior to running-in affects the surface morphology following the running-in process, which in turn influences the friction and wear characteristics of the workpiece. Therefore, the establishment of a model for running-in surface morphology prediction is important to investigate the process and optimize the surface design. Black-box models based on machine learning have robust complex object simulation performance. In this paper, five common machine learning methods are applied to establish running-in modeling performance based on surface morphology parameters. The support vector machine has the best model performance. The change law of the surface morphology parameters is obtained based on model testing,

SUBMITTER: Ge G 

PROVIDER: S-EPMC8489728 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature