Surface roughness measurement based on image texture analysis

In this paper, turning workpiece surface images were captured by the image acquisition system composed of digital microscopy, high-resolution camera and computer. the 14 texture feature parameters based on gray level co-occurrence matrix (GLCM) were extracted by using the method of statistical analysis, The variation between each texture parameter and arithmetic mean deviation (Ra) was investigated, thus the roughness of turning workpiece surface was evaluated qualitatively, the relationship between texture characteristic parameters and roughness evaluation index Ra was analyzed by using multiple regression method, and the linear and nonlinear regression testing model were bulit up. The result shows that the two models have good detection effect, and the nonlinear model performance better than the linear model.