Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/8359
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dc.contributor.authorÜnver, Hakkı Özgür-
dc.contributor.authorSener, Batıhan-
dc.date.accessioned2022-01-15T13:02:38Z-
dc.date.available2022-01-15T13:02:38Z-
dc.date.issued2021-
dc.identifier.issn0956-5515-
dc.identifier.issn1572-8145-
dc.identifier.urihttps://doi.org/10.1007/s10845-021-01839-3-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/8359-
dc.description.abstractDetection and avoidance of regenerative chatter play a crucial role in ensuring the high quality and efficiency of machining operations. Predominant analytical approaches provide stability lobe diagrams for machining processes. Deep learning is a general term given to the most recent and successful group of machine learning methods that proved great promise in many areas of human life. This study purposes a novel transfer learning framework that combines analytical solutions and convolution neural network (CNN) under a novel transfer learning framework. Stability lobes and numerical time-domain solutions of analytical methods are used to train and label, arguably one of the most successful CNN architectures, AlexNet. This approach eliminates the need for a time-consuming and costly experimental data collection phase for training. Furthermore, an ensemble empirical mode decomposition based signal pre-processing method is developed. An IMF-based multi-band ensemble approach is proposed where only intrinsic mode functions relevant to each modal frequency of the system are selected based on their entropy increase and used in training multiple AlexNet instances. The measured data were collected during shoulder milling from a CNC-vertical milling machine. The results revealed considerable success in several scenarios ranging from 82 to 100%, without using any experimentally measured data in training.en_US
dc.description.sponsorshipTUBITAK (The Scientific and Technological Research Council of Turkey) 1001 programTurkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) [118M414]en_US
dc.description.sponsorshipThe authors are thankful to Prof. Dr. Yusuf Altnta for his support in the dynamic characterization of the CNC-machine tool. This project is supported by TUBITAK (The Scientific and Technological Research Council of Turkey) 1001 program (No. 118M414).en_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofJournal of Intelligent Manufacturingen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectTransfer learningen_US
dc.subjectChatter detectionen_US
dc.subjectCNNen_US
dc.subjectEEMDen_US
dc.subjectIdentificationen_US
dc.subjectEemden_US
dc.titleA Novel Transfer Learning Framework for Chatter Detection Using Convolutional Neural Networksen_US
dc.typeArticleen_US
dc.departmentFaculties, Faculty of Engineering, Department of Computer Engineeringen_US
dc.departmentFaculties, Faculty of Engineering, Department of Mechanical Engineeringen_US
dc.departmentFakülteler, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümütr_TR
dc.departmentFakülteler, Mühendislik Fakültesi, Makine Mühendisliği Bölümütr_TR
dc.identifier.wosWOS:000697093700001en_US
dc.identifier.scopus2-s2.0-85115181096en_US
dc.institutionauthorÜnver, Hakkı Özgür-
dc.identifier.doi10.1007/s10845-021-01839-3-
dc.authorscopusid6603873269-
dc.authorscopusid57220450360-
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.scopusqualityQ1-
item.openairetypeArticle-
item.languageiso639-1en-
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
crisitem.author.dept02.7. Department of Mechanical Engineering-
Appears in Collections:Makine Mühendisliği Bölümü / Department of Mechanical Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
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