Tool Insert Wear Classification Using Statistical Descriptors and Neuronal Networks
The goal of this work is to automatically determine the level of tool insert wear based on images acquired using a vision system. Experimental wear was carried out by machining AISI SAE 1045 and 4140 steel bars in a precision CNC lathe and using Sandvik inserts of tungsten carbide. A Pulnix PE2015 B/W with an optic composed by an industrial zoom 70 XL to 1.5X and a diffuse lighting system was used for acquisition. After images were pre-processed and wear area segmented, several patterns of the wear area were obtained using a set of descriptors based on statistical moments. Two sets of experiments were carried out, the first one considering two classes, low wear level and high wear level, respectively; the second one considering three classes. Performance of three classifiers was evaluated: Lp2, k-nearest neighbours and neural networks. Zernike and Legendre descriptors show the lowest error rates using a MLP neuronal network for classifying.
KeywordsLearning Rate Neuronal Network Tungsten Carbide Zernike Moment Training Cycle
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