A self-organising neural network for chatter identification in milling
by T.C. Li, Y.S. Tarng, M .C. Chen
International Journal of Computer Applications in Technology (IJCAT), Vol. 9, No. 5/6, 1996

Abstract: An in-process milling chatter identification system based on a self-organising neural network using the adaptive resonance theory (ART2-A) is presented in the paper. The difference of the resultant cutting force signal in a revolution is utilised as the input pattern for the neural network to recognise the milling process with or without chatter. Experiments show that the new approach can correctly monitor chatter in milling operations.

Online publication date: Tue, 03-Jun-2014

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