Effect of different features to drill-wear prediction with back propagation neural network

Abstract In this paper, a back propagation neural network (BPNN) has been applied to predict the corner wear of a high speed steel (HSS) drill bit for drilling on different workpiece materials. Specially defined static and dynamic features extracted by a wavelet packet transform (WPT) from the resultant force converted from thrust and torque together with the cutting conditions (workpiece material, spindle speed, drill diameter, feed rate) are used as inputs to train the network to obtain a better output, drill corner wear. Drilling experiments have been carried out over a wide range and, features newly defined and conventional ones, features extracted from different frequency bands are compared.