Drill wear monitoring using neural networks

The primary objective of this research is to monitor drill wear on-line. In this paper, drill wear monitoring is carried out by measuring the thrust force and torque signals. In order to identify the tool wear conditions based on the signal measured, a neural network, using a cumulative back-propagation algorithm, is adopted. This paper also describes the experimental procedure used and presents the results obtained for establishing the neural network. The inputs to the neural network are the mean values of thrust force and torque, spindle rotational speed, feedrate and drill diameter. The neural network is trained to estimate the average drill wear. It is confirmed experimentally that the tool wear can be accurately estimated by the trained neural network. The accuracy of tool wear estimation using the neural network is superior to that using other regression models.