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-occurrence 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.
Keywordsroughness control textural descriptors gray level co-occurrence matrix k-nn neuronal network classification
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