Prediction of drill failure using features extraction in time and frequency domains of feed motor current

In this paper, a drill prefailure prediction method based on the feed motor current is proposed. The characteristic parameters of drill failure (CPDF) are defined in the time and frequency domains to express the features of the feed motor current at drill failure. In the time domain, the CPDFs represent the increase of average value and the standard deviation of the feed motor current at drill failure. In the frequency domain, the CPDFs represent the magnitude of vibration at the spindle rotational frequency and at two times this frequency of the feed motor current. The CPDFs are used as inputs to the neural network. The output of the neural network is defined as the drill state index (DSI). Drill failure is predicted by monitoring the number of times the DSI exceeds the threshold value of DSI. Experiments showed that the proposed algorithm could accurately identify impending failure before drill breakage regardless of cutting conditions and machine tool types.