Modeling and prediction of surface roughness using multiple regressions: A noncontact approach

In the present work, a machine vision system is introduced, which captures images and extracts surface texture features of machined surfaces. The texture feature parameters are extracted using the gray-level co-occurrence matrix and correlated with different surface roughness parameters recorded by a contact-type surface profilometer. The image acquisition carried out at different roughness levels in order to extract texture features. The variation between each texture features and surface roughness parameter is investigated. Multiple regression models are developed to predict the subjective estimation of surface roughness parameter (Ra) and qualitative detection of the degree of surface roughness. It is observed that the linear detection model shows better performance characteristics compared with a nonlinear detection model. The comparison between measured and predicted results shows that the linear detection model had a maximum relative error of 2.01%, drastically better than nonlinear detection model of -9.60% error parts, hence indicating better surface detection capability over the nonlinear detection model. The results demonstrate that the prediction of surface roughness using linear regression model is a reliable approach of noncontact measurement.