SELF-ORGANIZING NEURAL-NETWORK APPLICATION TO DRILL WEAR CLASSIFICATION

The article describes an application of a simulated neural network to drill wear classification from cutting force signals generated by the drilling process. As the input to the neural network, a multicomponent vector composed of a sensory part and a descriptive part is used. The components of the sensory part represent characteristic features of t he cutting momentum and the feed force power spectra, while the descri ptive part encodes the corresponding drill wear class. During adaptati on, the self-organizing neural network is used to form a set of protot ype vectors representing an empirical model of the observed drilling p rocess. The model is used in the analysis mode of the system for an on -line classification of the drill wear from the cutting forces. The pe rformance of the developed information processing system is experiment ally demonstrated by classification of drill wear during machining on a steel workpiece.