Effect of surface lay in the surface roughness evaluation using machine vision

This work explores the influence of orientation of surface lay pattern of the machined components, while quantifying the surface roughness using machine vision approach. The surface images are captured from milled low carbon steel specimens with different roughness values using a vision system with coaxial lighting arrangement at different angular orientations of the work pieces (0°, 30°, 45°, 60°, 90°, 120°, 135°, 150°, and 180°). The captured images are subjected to preprocessing in order to retain the frequency components that attribute to roughness using a Gaussian filter by adapting the filtering procedures specified in ISO 4288. Numerous image based parameters such as gray level average (G_a), gray level co-occurrence matrix based image quantification parameters namely contrast, correlation, energy or uniformity, maximum probability and differential box courting based fractal dimension are computed from the surface images captured at different angular positions of the work piece. The computed vision based parameters are compared and correlated with the roughness average (R_a) obtained using a stylus instrument and the results are analyzed. The results clearly indicated that it is important to consider the orientation of the work piece when the machine vision approach is used to quantify the surface texture parameters.