ToolW earM onitoring Based on Integrated NeuralNetworks

A tool wear condition monitoring approach based on integrated neural networks was proposed to recognize and predicttoolwearconditions inmilling operations. In this approach, vibration and cutting force signals are decomposed into time sequences in different frequency bands bywavelet packet transform, and the rootmean square values of each signal in three frequency bands, extracted from decomposed signals, with a close relation towear conditions are selected asmonitoring features. The final recognition results of tool wear are given by the integrated neural networks through the combination of signals and the decision fusion of different subnets. Experiments and simulations show that the proposed approach canmeet the requirements ofon-linemonitoring of toolwear conditions.