Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.11851/5526
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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Şener, B. | - |
dc.contributor.author | Güdelek, M. U. | - |
dc.contributor.author | Özbayoğlu, Ahmet Murat | - |
dc.contributor.author | Ünver, Hakkı Özgür | - |
dc.date.accessioned | 2021-09-11T15:19:10Z | - |
dc.date.available | 2021-09-11T15:19:10Z | - |
dc.date.issued | 2021 | en_US |
dc.identifier.issn | 0263-2241 | - |
dc.identifier.uri | https://doi.org/10.1016/j.measurement.2021.109689 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.11851/5526 | - |
dc.description.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 | en_US |
dc.description.sponsorship | Türkiye Bilimsel ve Teknolojik Araştirma Kurumu: 118M414 | en_US |
dc.language.iso | en | en_US |
dc.publisher | Elsevier B.V. | en_US |
dc.relation.ispartof | Measurement: Journal of the International Measurement Confederation | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | CWT | en_US |
dc.subject | DCNN | en_US |
dc.subject | Deep learning | en_US |
dc.subject | Milling | en_US |
dc.subject | Regenerative chatter | en_US |
dc.title | A novel chatter detection method for milling using deep convolution neural networks | en_US |
dc.type | Article | en_US |
dc.department | Faculties, Faculty of Engineering, Department of Computer Engineering | en_US |
dc.department | Faculties, Faculty of Engineering, Department of Mechanical Engineering | en_US |
dc.department | Faculties, Faculty of Engineering, Department of Artificial Intelligence Engineering | en_US |
dc.department | Fakülteler, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü | tr_TR |
dc.department | Fakülteler, Mühendislik Fakültesi, Makine Mühendisliği Bölümü | tr_TR |
dc.department | Fakülteler, Mühendislik Fakültesi, Yapay Zeka Mühendisliği Bölümü | tr_TR |
dc.identifier.volume | 182 | en_US |
dc.identifier.wos | WOS:000684200500008 | en_US |
dc.identifier.scopus | 2-s2.0-85107718180 | en_US |
dc.institutionauthor | Güdelek, Mehmet Uğur | - |
dc.institutionauthor | Özbayoğlu, Aahmet Murat | - |
dc.institutionauthor | Ünver, Hakkı Özgür | - |
dc.identifier.doi | 10.1016/j.measurement.2021.109689 | - |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.identifier.scopusquality | Q1 | - |
item.openairetype | Article | - |
item.languageiso639-1 | en | - |
item.grantfulltext | none | - |
item.fulltext | No Fulltext | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.cerifentitytype | Publications | - |
crisitem.author.dept | 02.1. Department of Artificial Intelligence Engineering | - |
crisitem.author.dept | 02.7. Department of Mechanical Engineering | - |
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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