Being able to identify machining processes that produce specific machined surfaces is crucial in modern manufacturing production. Image processing and computer vision technologies have become indispensable tools for automated identification with benefits such as reduction in inspection time and avoidance of human errors due to inconsistency and fatigue. In this paper, the Support Vector Machine (SVM) classifier with various kernels is investigated for the categorization of machined surfaces into the six machining processes of Turning, Grinding, Horizontal Milling, Vertical Milling, Lapping, and Shaping. The effectiveness of the gray-level histogram as the discriminating feature is explored. Experimental results suggest that the SVM with the linear kernel provides superior performance for a dataset consisting of 72 workpiece images.

Texture classification is one of the most important clues of visual processing applications .In this paper, we present a comparison between the most two popular supervised texture classification methods based on the feed forward Artificial Neural Network (ANN) and the multi-class Support Vector Machine (SVM). Five of the most common used features extraction approaches were chosen in order to extract input vectors of different sizes for both classifiers. These approaches are namely gray level histogram, edge detection, and co-occurrence matrices, besides Gabor and Biorthogonal wavelet transformations. Experiments are conducted on two different datasets the first one is engineering surface textures produced by different machining processes, and the second was taken from Brodatz (1966) textures album. The classification accuracy rate is calculated for ANN and SVM in order to measure the efficiency of each technique based on the several features extraction methods. The results show that SVM with its linear and polynomial kernels is higher in classification accuracy and faster in training time.