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7112504 
Journal Article 
Tool condition monitoring using K-star algorithm 
Painuli, S; Elangovan, M; Sugumaran, V; , 
2014 
Yes 
Expert Systems with Applications
ISSN: 0957-4174 
PERGAMON-ELSEVIER SCIENCE LTD 
OXFORD 
2638-2643 
WOS:000331018000003 
Cutting tools are required for day to day activities in manufacturing. Continuous machining operations lead tool to undergo wear. Worn out tools effect surface finish during machining. The dimensional accuracy of components is also compromised. Robust tool health is vital for better productivity. Hence, an online system condition monitoring of tools is the need of hour, promising reduction in maintenance cost with a greater productivity saving both time and money. This paper presents the classification performance of K-star algorithm. A set of statistical features extracted from vibration signals (good and faulty conditions) form the input to algorithm. In the present study, the K-star algorithm is able to achieve 78% classification accuracy. (C) 2013 Elsevier Ltd. All rights reserved.