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|Title:||Feature analysis in tool condition monitoring: A case study in titanium machining||Authors:||Sun, J.
Tool condition monitoring
|Issue Date:||2012||Citation:||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|
|Appears in Collections:||Staff Publications|
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