Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/5526
Title: A novel chatter detection method for milling using deep convolution neural networks
Authors: Şener, B.
Güdelek, M. U.
Özbayoğlu, Ahmet Murat
Ünver, Hakkı Özgür
Keywords: CWT
DCNN
Deep learning
Milling
Regenerative chatter
Issue Date: 2021
Publisher: Elsevier B.V.
Abstract: Regenerative chatter is harmful to machining operations, and it must be avoided to increase production efficiency. The recent success of deep learning methods in many fields also presents an excellent opportunity to advance chatter detection and its wider industrial adoption. In this work, a chatter detection method based on deep convolutional neural network (DCNN) is presented. The method uses a cardinal model-based chatter solution to precisely label regenerative chatter levels. During milling, vibration data are collected via a non-invasive data acquisition strategy. Considering nonlinear and non-stationary characteristics of chatter, continuous wavelet transform (CWT) is used as the pre-processing technique to reveal critical chatter rich information. Afterward, the images are used for training and test of the developed DCNN. The validation of the method revealed that when cutting parameters are also included as input features to the DCNN, average accuracy reached to 99.88%. © 2021 Elsevier Ltd
URI: https://doi.org/10.1016/j.measurement.2021.109689
https://hdl.handle.net/20.500.11851/5526
ISSN: 0263-2241
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
Makine Mühendisliği Bölümü / Department of Mechanical Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
Yapay Zeka Mühendisliği Bölümü / Department of Artificial Intelligence Engineering

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