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Early detection of drilling tool wear by vibration data acquisition and classification
Dheeraj Simon G,
Published in Elsevier BV
2019
Volume: 21
   
Pages: 60 - 65
Abstract
Drilling is a frequently used machining operation in manufacturing industries. Continuous operation of the drilling tool results in tool wear which affects the surface finish and dimensional accuracy of the work. In this paper, the feasibility of early detection of limiting tool wear has been explored. Tool performance is monitored by acquiring the vibration signals developed during the drilling of stainless steel plates using HSS tool. Statistical features are extracted from the vibration signals based on which the tool wear condition is differentiated by K-Star classifier. The classification accuracy obtained is 79.56%. © 2019 Society of Manufacturing Engineers (SME)
About the journal
JournalData powered by TypesetManufacturing Letters
PublisherData powered by TypesetElsevier BV
ISSN2213-8463
Open Access0