Title: Feature analysis in tool condition monitoring: a case study in titanium machining

Authors: Jie Sun; Yoke San Wong; Geok Soon Hong; Mustafizur Rahman

Addresses: Department of Mechanical Engineering, National University of Singapore, 10 Kent Ridge Crescent, 119260, Singapore. ' Department of Mechanical Engineering, National University of Singapore, 10 Kent Ridge Crescent, 119260, Singapore. ' Department of Mechanical Engineering, National University of Singapore, 10 Kent Ridge Crescent, 119260, Singapore. ' Department of Mechanical Engineering, National University of Singapore, 10 Kent Ridge Crescent, 119260, Singapore

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.

Keywords: tool condition monitoring; TCM; feature selection; sensor fusion; feature analysis; titanium machining; tool wear; tool monitoring; acoustic emission; cutting force; sensor signals.

DOI: 10.1504/IJCAT.2012.050707

International Journal of Computer Applications in Technology, 2012 Vol.45 No.2/3, pp.177 - 185

Available online: 30 Nov 2012 *

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