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|Title:||A comprehensive identification of tool failure and chatter using a parallel multi-ART2 neural network||Authors:||Li, X.Q.
|Issue Date:||May-1998||Citation:||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|
|Appears in Collections:||Staff Publications|
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