Application of wavelet packet transform based Normalised Radial Basis Function Network in a machining process

In this work, an attempt has been made to develop a drill wear prediction system. A Hall-effect current sensor has been used for acquiring motor current signals during drilling under different cutting conditions. Wavelet packet transform has been used on the acquired current signals to extract features. A normalised Radial Basis Function (RBF) neural network model has then been developed to correlate the extracted features with drill wear. The proposed network outperforms the standard RBF neural network in terms of training error and also in terms of prediction error.