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Condition Monitoring of Single Point Cutting Tools Based on Machine Learning Approach
Gangadhar N, Kumar H, Narendranath S,
Published in International Institute of Acoustics and Vibration (IIAV)
2018
Volume: 23
   
Issue: 2
Pages: 131 - 137
Abstract
This paper presents the use of multilayer perceptron (MLP) for fault diagnosis through a histogram feature extracted from vibration signals of healthy and faulty conditions of single point cutting tools. The features were extracted from the vibration signals, which were acquired while machining with healthy and different worn-out tool conditions. Principle component analysis (PCA) used to select important extracted features. The artificial neural network (ANN) algorithm was applied as a fault classifier in order to know the status of cutting tool conditions. The accuracy of classification with MLP was found to be 82.5 %, which validates that the proposed approach is an effective method for fault diagnosis of single point cutting tools. © 2018 International Institute of Acoustics and Vibrations. All Rights Reserved.
About the journal
JournalThe International Journal of Acoustics and Vibration
PublisherInternational Institute of Acoustics and Vibration (IIAV)
ISSN1027-5851
Open AccessNo