Surface Finish Control in Machining Processes Using Haralick Descriptors and Neuronal Networks

This paper presents a method to perform a surface finish control using a computer vision system. The goal pursued was to design an acceptance criterion for the control of surface roughness of steel parts, dividing them in those with low roughness acceptable class and those with high roughness defective class. We have used 143 images obtained from AISI 303 stainless steel machining. Images were described using three different methods texture local filters, the first four Haralick descriptors from the gray-level co-ocurrence matrix and a 20 features vector obtained from the first subband of a wavelet transform of the original image and also the gray-level original image. Classification was conducted using K-nn and Neuronal Networks. The best error rate 4.0% with k-nn was achieved using texture descriptors. With the neuronal network, an eight node hidden layer network using Haralick descriptors leads to the optimal configuration 0.0% error rate -.