Abstract
Cutting chatter is violent self-excited vibration between tool and work piece, which will seriously degrade the machining surface quality and health of machine tool. Online monitoring of chatter phenomenon can feed back the control system to adjust processing parameters dynamically for chatter suppression. In this article, a short-time difference spectrum analysis (STDSA) is proposed for early chatter identification, which can recognize chatter frequency and track its amplitude from low SNR vibration signal at early stage. The method is verified by both simulated vibration signals and machining vibration signals collected from an axle lathe. A comparison between classic variational mode decomposition (VMD) and STDSA is given to illustrate its performance. The results indicate that the method is robust to noise and has a little warning time delay, which makes it suitable for the online monitoring of cutting chatter in an industrial production environment.
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