In this paper, an online tool condition monitoring system (TCMS) for drilling is presented. The method is based on monitoring the spindle and feed motor currents. Root mean square (RMS) values of the spindle and feed motor currents, drill diameter, spindle speed and feed rate are the inputs to the network, and drill wear is the output. Drilling experiments were carried out over a wide range of cutting conditions to map the relationship between the input information and a tool wear. The performance and the architecture of the neural network have been validated with experiments, and a good agreement in an estimation of the tool condition was found. The results show that this method can be effectively used to verify and determine the tool wear.
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