Prediction of Surface Topography at the End of Sliding Running-In Wear Based on Areal Surface Parameters

Abstract

Running in is a complex process, and it significantly influences the performance and service life of wear components as the initial phase of the entire wear process. Surface topography is an important feature of wear components. Therefore, it is reasonable to investigate the running-in process with the help of surface topography for improvement. Because the surface roughness after running in is independent of the nature of initial roughness, it is difficult to predict the surface topography after running in based on unworn surface topography. Aiming to build a connection of surface topographies before and after the running-in process, a black-box model predicting surface topography after the running-in process was established based on least-squares support vector machine (LS-SVM), and the areal surface evaluation parameters were adopted as model variables. To increase the adaptability of the predictive model, the main factors of the work condition were also taken into consideration. The prediction effect and sensitivity of the model were tested and analyzed. The analysis indicates that the hybrid property of surface topographies before and after running in is closely related. Moreover, the surface topography after running in is influenced more by the initial surface topography than by the work condition.