Please use this identifier to cite or link to this item: https://doi.org/10.1504/IJCAT.2012.050707
Title: Feature analysis in tool condition monitoring: A case study in titanium machining
Authors: Sun, J. 
Wong, Y.S. 
Hong, G.S. 
Rahman, M. 
Keywords: Feature selection
Sensor fusion
TCM
Tool condition monitoring
Issue Date: 2012
Source: Sun, J.,Wong, Y.S.,Hong, G.S.,Rahman, M. (2012). Feature analysis in tool condition monitoring: A case study in titanium machining. International Journal of Computer Applications in Technology 45 (2-3) : 177-185. ScholarBank@NUS Repository. https://doi.org/10.1504/IJCAT.2012.050707
Abstract: Due to the rapid wear of the cutting tools when machining titanium alloy, tool condition monitoring (TCM) is most useful to avoid workpiece damage and maximise machining productivity. This paper uses sensor signals and feature analysis to identify a feature set for effective TCM. Firstly, basic requirements of sensor signals in tool condition identification are discussed, and the suitability of two candidate signals (acoustic emission and cutting force) commonly employed for machining monitoring are critically analysed. Their effectiveness in TCM is investigated based on extracted features of these signals, singly or in combination. Experimental results based on titanium machining, which is an expensive process with high tool wear, indicate that this proposed method is capable to determine a suitable sensing method and an effective feature set to identify tool condition. Copyright © 2012 Inderscience Enterprises Ltd.
Source Title: International Journal of Computer Applications in Technology
URI: http://scholarbank.nus.edu.sg/handle/10635/60304
ISSN: 09528091
DOI: 10.1504/IJCAT.2012.050707
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