Abstract
Computer vision technology has maintained tremendous vitality in many fields. Several investigations have been performed to inspect surface roughness based on computer vision technology. This work presents a new approach for surface roughness characterization using computer vision and image processing techniques. A vision system has been introduced to capture images for surfaces to be characterized and a software has been developed to analyze the captured images based on the gray level co-occurrence matrix (GLCM).
Three standard specimens and 10 machined samples with different roughness values have been characterized by the presented approach. Three-dimensional plots of the GLCMs for various captured images have been introduced, compared and discussed. In addition, some statistical parameters (maximum occurrence of the matrix, maximum occurrence position and standard deviation of the matrix) have been calculated from the GLCMs and compared with the arithmetic average roughness
R
a. Furthermore, a new parameter called maximum width of the matrix is introduced to be used as an indicator for surface roughness.