A New Numerical Simulation Method for 3D Rough Surface Topography of Shot Peening Parts with Specified 3D Roughness Spatial Parameters

According to random process theory, the existing autocorrelation function (ACF) expression that characterizes the spatial features of the shot peening (SP) surface topography makes it difficult to constrain the 3D roughness spatial parameters defined in ISO 25178, which restricts the correlation studies between surface topography and service performance. This paper introduces a new ACF expression for reconstructing the SP surface topography with specified spatial parameters. Based on the new expression, a numerical simulation method for stratified surface topography applicable to SP after finishing is introduced. The main idea is to perform feature extraction and feature modeling on the measured surface with the help of machine learning. The new method is applied to the numerical simulation of the SP and grinding-shot peening (Gr-SP) surface topography with a coverage of 200%. The relative error in height distribution and spatial parameters between the measured and simulated surface topography are less than 5%. The new method of height distribution and spatial parameters active design is provided to study the correlation between surface topography and service performance of shot peening parts.