Flute breakage detection during end milling using Hilbert–Huang transform and smoothed nonlinear energy operator

Abstract Real time tool condition monitoring has great significance in modern manufacturing processes. In order to prevent possible damages to the workpiece or the machine tool, reliable monitoring techniques are required to provide fast response to the unexpected tool failure. Milling is one of the most fundamental machining operations. During the milling process, the current of feed motor is weakly related to the cutter condition, the change of power consumption is not significant to identify tool condition. Thus, current of motor-based tool condition still requires some new approaches to sort out significant pattern that could be employed to indicate tool condition. In this paper, a new approach is proposed to detect end mill flute breakage via the feed-motor current signals, which implements Hilbert–Huang transform (HHT) analysis and a smoothed nonlinear energy operator (SNEO) to extract the crucial characteristics from the measured signals to indicate tool breakage. Experiments on a CNC Vertical Machining Centre are presented to show the algorithm performance. The results show that this method is feasible and can accurately and efficiently monitor the conditions of the end mill under varying cutting conditions.

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