A self–organising neural network for chatter identification in milling. (4th August 2014)

• Record Type: Journal Article
• Authors: Li, T.C.; Tarng, Y.S.; Chen, M .C.
• Is Part Of: International journal of computer applications technology. Volume 9:Number 5/6(1996)
• Page Start: 239
• Page End: 248
• Publication Date: 2014-08-04
• Subjects: chatter identification -- milling -- process monitoring -- neural networks -- adaptive resonance theory -- ART -- cutting conditions -- machining vibration; Technology -- Data processing -- Periodicals; 620.00285
• Languages: English
• ISSNs: 0952-8091

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.