Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/6410
Title: Comparison of Classifiers for Chatter Detection
Authors: Tuğci, Recep
Çelen, V. Burak A.
Özbayoğlu, Murat
Keywords: Chatter detection
Pattern Recognition
Support Vector Machines
Neural Networks
Perceptron
Issue Date: 2013
Publisher: IEEE
Source: 21st Signal Processing and Communications Applications Conference (SIU) -- APR 24-26, 2013 -- CYPRUS
Series/Report no.: Signal Processing and Communications Applications Conference
Abstract: Unstable machine cutting causes chatter and reduces quality of the production. Therefore it must be detected. Several techniques have been presented for this reason. The aim of this study is to determine the data, features and classifiers which fit on chatter detection. In order to detect chatter; acoustic emission and vibration data are collected, several features are generated which belong to time and frequency domains. Then the best features are chosen via k- means clustering, support vector machines, feed forward back propagation neural networks and perceptron classifiers. The performance of the system is analyzed. As results of the study, the best data, features and classifiers are chosen for the chatter detection.
URI: https://hdl.handle.net/20.500.11851/6410
ISBN: 978-1-4673-5563-6; 978-1-4673-5562-9
ISSN: 2165-0608
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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