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 char-acteristic features ofthe cutting momentum and the feed force power spectra, while the descriptive part encodes the corresponding drill wear class. During adaptation, the self-organizing neural network is used to form a set of prototype vectors rep-resenting an empirical model of the observed drilling process. 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 performance ofthe developed information processing system is experimentally demonstrated by classification of drill wear during machining on a steel workpiece.