Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/57896
Title: An intelligent sensor system approach for reliable tool flank wear recognition
Authors: Niu, Y.Y.M.
Wong, Y.S. 
Hong, G.S. 
Keywords: Feature extraction
Intelligent sensors
Neural network
Reliability
Tool wear monitoring
Issue Date: 1998
Source: Niu, Y.Y.M.,Wong, Y.S.,Hong, G.S. (1998). An intelligent sensor system approach for reliable tool flank wear recognition. International Journal of Advanced Manufacturing Technology 14 (2) : 77-84. ScholarBank@NUS Repository.
Abstract: An intelligent sensor system approach for reliable flank wear monitoring in turning is described. Based on acoustic emission and force sensing, an intelligent sensor system integrates multiple sensing, advanced feature extraction and information fusion methodology. Spectral, statistical and dynamic analysis have been used to determine primary features from the sensor signals. A secondary feature refinement is further applied to the primary features in order to obtain a more correlated feature vector for the tool flank wear process. An unsupervised ART2 neural network is used for the fusion of AE and force information and decision-making of the tool flank wear stale. The experimental results confirm that the developed intelligent sensor system can be reliably used to recognise the tool flank wear state over a range of cutting conditions.
Source Title: International Journal of Advanced Manufacturing Technology
URI: http://scholarbank.nus.edu.sg/handle/10635/57896
ISSN: 02683768
Appears in Collections:Staff Publications

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