On-line drilling chatter recognition and avoidance using an ART2—A neural network

In the paper, an adaptive resonance theory (ART2-A) neural network is applied to on-line recognition and avoidance of drilling chatter. It is shown that the ART2-A neural network can adaptively learn the features of the thrust force spectrum in a drilling process. As a result, drilling chatter can be automatically detected when a chatter feature starts to appear in the thrust force spectrum. Once chatter is detected, a spindle speed regulation method to suppress chatter is used. Experiments show that this new developed system can monitor and suppress drilling chatter efficiently even under varying cutting conditions.

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