Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/8657
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dc.contributor.authorÜnver, Hakkı Özgür-
dc.contributor.authorSener, Batihan-
dc.date.accessioned2022-07-30T16:43:44Z-
dc.date.available2022-07-30T16:43:44Z-
dc.date.issued2022-
dc.identifier.citationUnver, H. O., & Sener, B. (2022). Exploring the Potential of Transfer Learning for Chatter Detection. Procedia Computer Science, 200, 151-159.en_US
dc.identifier.issn1877-0509-
dc.identifier.urihttps://doi.org/10.1016/j.procs.2022.01.214-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/8657-
dc.description3rd International Conference on Industry 4.0 and Smart Manufacturing (ISM) -- NOV 17-19, 2021 -- Upper Austria Univ Appl Sci, Hagenberg Campus, Linz, AUSTRIAen_US
dc.description.abstractChatter detection and avoidance are indispensable for many industries that rely on the machining process. The physics-based analytical models and recently successful machine learning methods can provide solutions using data from a unique setting. When the primary conditions of machining alter, new data needs to be collected, and analysis/training should be revised. Unfortunately, data collection is time-consuming and expensive for all machine learning applications. Therefore, broader applications of these methods are usually hindered at high production rate machining shops. Transfer learning aims to attenuate this critical barrier of machine learning implementations by transferring knowledge generated from a source domain to a different but related domain. As the concept has immense potential as an accelerator for machine learning applications, it has many prospects in Industry 4.0 framework. This article provides an introduction to transfer learning and briefly overviews its categorizations. Afterward, its potential for chatter detection is explored, and potential strategies are exemplified. Recent studies in the literature within the strategies are briefly presented as well. (C) 2022 The Authors. Published by Elsevier B.V.en_US
dc.description.sponsorshipTUBITAK (The Scientific and Technological Research Council of Turkey) [118M414]en_US
dc.description.sponsorshipThis study is funded by TUBITAK (The Scientific and Technological Research Council of Turkey) through project grant no. 118M414.en_US
dc.language.isoenen_US
dc.publisherElsevier Science Bven_US
dc.relation.ispartof3rd International Conference On Industry 4.0 and Smart Manufacturingen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectTransfer Learningen_US
dc.subjectChatteren_US
dc.subjectMachiningen_US
dc.subjectMachine Learningen_US
dc.titleExploring the Potential of Transfer Learning for Chatter Detectionen_US
dc.typeConference Objecten_US
dc.departmentFakülteler, Mühendislik Fakültesi, Makine Mühendisliği Bölümüen_US
dc.departmentFaculties, Faculty of Engineering, Department of Mechanical Engineeringen_US
dc.identifier.volume200en_US
dc.identifier.startpage151en_US
dc.identifier.endpage159en_US
dc.identifier.wosWOS:000777601300016en_US
dc.identifier.scopus2-s2.0-85127741220en_US
dc.institutionauthorÜnver, Hakkı Özgür-
dc.identifier.doi10.1016/j.procs.2022.01.214-
dc.authorscopusid6603873269-
dc.authorscopusid57220450360-
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.identifier.scopusquality--
item.openairetypeConference Object-
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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