Tool insert wear classification using statistical descriptors and neuronal networks

Abstract

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. © Springer-Verlag Berlin Heidelberg 2005.