Multi-process precision surface topography characterization and simulation based on multi-Gaussian and multi-fractal model

Abstract

Surfaces produced by multi-process machining often exhibit both multi-Gaussian and multi-fractal features, which together govern key functional properties such as contact and friction. However, current methods for characterizing and simulating these surfaces primarily focus on the multi-Gaussian features, often overlooking the multi-fractal features and failing to effectively establish correlation between them. Furthermore, traditional multi-fractal simulation methods suffer from discontinuous transitions. To address these issues, the multi-Gaussian surfaces were first characterized and subsequently generated using the Probability Material Ratio Curve (PMRC). The intrinsic relationship between multi-Gaussian and multi-fractal features was also investigated. Furthermore, a novel characterization method based on a fitted Power Spectral Density (PSD) function is proposed. This method uses a continuous function to accurately describe multi-fractal features, enabling the corresponding surface simulation. On this basis, an iterative reordering algorithm has been introduced to effectively integrate the features of the simulated multi-Gaussian surface with those of the multi-fractal surface. Simulation and experimental results demonstrate that the proposed framework can accurately characterize the topography of multi-processed surfaces and achieve high-fidelity surface simulation using the characterized parameters as inputs. This research overcomes the limitations of single-characteristic modeling by enabling the parametric characterization and simulation of multi-processed surfaces with hybrid multi-Gaussian and multi-fractal features, thus providing an accurate way to predict their functional performance.