Texture-Aware Ridgelet Transform and Machine Learning for Surface Roughness Prediction

Abstract

Quantification of machined surface roughness is critical to enabling estimation of part performance such as tribology and fatigue. As a contactless alternative to the traditional contact profilometry, photographic methods have been widely applied due to the advancement of image processing and machine learning techniques that allow the analysis of surface characteristics embedded in optical images and association of these characteristics with surface roughness. The state-of-the-art of photographic methods make extensive use of 2-D wavelet transform for image processing. However, a 2-D wavelet is often limited in capturing line patterns that are prevalent in the machined surface due to its radially symmetric nature, leading to suboptimal surface characterization. In addition, surface roughness prediction is primarily carried out as point prediction using machine learning methods which do not account for uncertainty in the models and data. To address these limitations, this study presents a ridgelet transform (RT)-based method for machined surface characterization. RT automatically detects the dominant line patterns, i.e., texture, in surface images and extracts topological features, such as the constituent spatial frequencies embedded in the surface profile along the direction that is most relevant for inducing surface roughness. The extracted texture-aware features are then used as inputs to random forest and kernel density estimation for surface roughness prediction and uncertainty quantification. Evaluation using experimental data shows that the developed method predicts surface roughness with an error of 0.5%, outperforming existing techniques and demonstrating the potential of RT as a viable technique for machined surface analysis.