Publication Type:

Journal Article

Source:

Expert Systems with Applications, Volume 41, Number 6, p.2638-2643 (2014)

URL:

https://www.scopus.com/inward/record.uri?eid=2-s2.0-84889659961&partnerID=40&md5=f35e0beb86b02a8d2fdb2cd0c2696663

Keywords:

Algorithms, Classification accuracy, Classification performance, Condition monitoring, Dimensional accuracy, K-star, Learning systems, Machining operations, Productivity, Stars, Tool condition monitoring, Tool wear, Tools, Vibration signal

Abstract:

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. © 2013 Elsevier Ltd. All rights reserved.

Notes:

cited By 8

Cite this Research Publication

Sa Painuli, Elangovan, Mb, and Sugumaran, Va, “Tool condition monitoring using K-star algorithm”, Expert Systems with Applications, vol. 41, pp. 2638-2643, 2014.

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