A self–organising neural network for chatter identification in milling. (4th August 2014) - Li, T.C.; Tarng, Y.S.; Chen, M .C.
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.