Title: A self-organising neural network for chatter identification in milling

Authors: T.C. Li, Y.S. Tarng, M .C. Chen

Addresses: Department of Mechanical Engineering, St. John's & St. Mary's Institute of Technology, Tamsui, Taipei, Taiwan. ' Department of Mechanical Engineering, National Taiwan Institute of Technology, Taipei, Taiwan. ' Department of Mechanical Engineering, National Taiwan Institute of Technology, Taipei, Taiwan

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.

Keywords: chatter identification; milling; process monitoring; neural networks; adaptive resonance theory; ART; cutting conditions; machining vibration.

DOI: 10.1504/IJCAT.1996.062315

International Journal of Computer Applications in Technology, 1996 Vol.9 No.5/6, pp.239 - 248

Available online: 03 Jun 2014 *

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