Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/54002
Title: A comprehensive identification of tool failure and chatter using a parallel multi-ART2 neural network
Authors: Li, X.Q. 
Wong, Y.S. 
Nee, A.Y.C. 
Issue Date: May-1998
Source: Li, X.Q.,Wong, Y.S.,Nee, A.Y.C. (1998-05). A comprehensive identification of tool failure and chatter using a parallel multi-ART2 neural network. Journal of Manufacturing Science and Engineering, Transactions of the ASME 120 (2) : 433-442. ScholarBank@NUS Repository.
Abstract: Tool failure and chatter are two major problems during machining. To detect and distinguish the occurrences of these two abnormal conditions, a novel parallel multi-ART2 neural network has been developed. An advantage of this network is more reliable identification of a variety of complex patterns. This is due to the sharing of multi-input feature information by its multiple ART2 subnetworks which allow for finer vigilance thresholds. Using the maximum frequency-band coherence function of two acceleration signals and the relative weighted frequency-band power ratio of an acoustic emission signal as input feature information, the network has been found to identify various tool failure and chatter states in turning operations with a total of 96.4% success rate over a wide range of cutting conditions, compared to that of 80.4% obtainable with the single-ART2 neural network.
Source Title: Journal of Manufacturing Science and Engineering, Transactions of the ASME
URI: http://scholarbank.nus.edu.sg/handle/10635/54002
ISSN: 10871357
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