Effect of Illumination Systems on Statistical Texture Parameters Based Clustering and Discrimination of Machined Surfaces Using Machine Vision

Abstract

Inspection of surface quality using machine vision is based on the principle of surface characterization using texture parameters deduced from intensity values of images captured by the system. Illumination system plays significant role in deciding the performance and robustness of machine vision by controlling quality of image acquisition. This paper presents an experimental study of the effect of various illumination systems on image acquisition. Images of flat machined surfaces under five different illumination setups: ambient light, dark field, partial bright field with spotlight, partial bright field with tubelight and partial bright field with diffuse surface light; are grouped into three surface classes based on first and second order statistical texture parameters. The result showed that partial bright field with diffuse surface light provides maximum performance during image acquisition providing highest clustering accuracy. The images under this optimum setup are further analyzed using multiple discriminant analysis for determining the parameters significantly contributing to discrimination. The results showed that average height departure, root mean square, maximum peak to valley, skewness based on line samples; maximum peak to valley, skewness, kurtosis for surface and gray level co-occurrence matrix based contrast, correlation, energy, homogeneity effectively contributed to characterization of texture for discrimination.