Applying Gaussian mixture models for enhanced characterization of featured surfaces and mixed lubrication analysis

Abstract

Understanding surface topography is vital for optimizing the performance of engineering components. Featured surfaces, with distinct patterns and textures, have garnered attention for their potential to reduce friction and wear. However, accurately describing their topography poses challenges, necessitating effective segmentation methods in many applications. This paper proposes utilizing the Gaussian Mixture Model (GMM) clustering method as a novel approach for surface metrology analysis of featured surfaces. The GMM provides an approach to identify and analyze specific surface features and enhance comprehension of their contributions to functionality. The paper presents a comprehensive methodology involving surface characterization, GMM clustering, plateau reference plane location, and calculation of essential topography parameters. Results from four different surfaces are discussed, demonstrating the effectiveness of the proposed GMM-based methodology in segmenting plateau regions, grooves, and porosity.