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Radial Basis Function Network Based Monitoring of Tool Wear States

In this paper, combination of Wavelet Packet Decomposition n (WPD) and Neural Networks (NN) was used to identification the experimental cutting torque data of drilling operations previously. It consists of three steps: firstly, decomposition cutting torque from the original signals by WPD; secondly, extracting wavelet coefficients of different wear states (i.e., slight, normal, or severe wear) with signal features adapting to Welch spectrum; finally, the spectrum feature vectors identify by using the Radial basis function neural network (RBFNN). The experiments on different tool wears states of Monitoring and identification are significant and effective.