Tool condition classification in turning process using hidden Markov model based on texture analysis of machined surface images

Tool condition classification in turning process using hidden Markov model based on texture... •Condition monitoring of cutting tool by processing machined surface images is done.•Image features are extracted by applying gray level co-occurrence technique on machined surface images.•Feature selection is performed using Fisher’s discriminant ratio method.•Hidden Markov model is applied on selected features for tool state classification.•Three levels of tool states are classified with maximum of 97% accuracy into sharp, semi-dull and dull tool states. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Measurement Elsevier

Tool condition classification in turning process using hidden Markov model based on texture analysis of machined surface images

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Publisher
Elsevier
Copyright
Copyright © 2016 Elsevier Ltd
ISSN
0263-2241
eISSN
1873-412X
D.O.I.
10.1016/j.measurement.2016.05.022
Publisher site
See Article on Publisher Site

Abstract

•Condition monitoring of cutting tool by processing machined surface images is done.•Image features are extracted by applying gray level co-occurrence technique on machined surface images.•Feature selection is performed using Fisher’s discriminant ratio method.•Hidden Markov model is applied on selected features for tool state classification.•Three levels of tool states are classified with maximum of 97% accuracy into sharp, semi-dull and dull tool states.

Journal

MeasurementElsevier

Published: Aug 1, 2016

References

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