Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/3832
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dc.contributor.authorSerin, G.-
dc.contributor.authorŞener, B.-
dc.contributor.authorÖzbayoğlu, Ahmet Murat-
dc.contributor.authorÜnver, Hakkı Özgür-
dc.date.accessioned2020-10-21T10:05:17Z-
dc.date.available2020-10-21T10:05:17Z-
dc.date.issued2020-07-
dc.identifier.citationSerin, G., Sener, B., Ozbayoglu, A. M., and Unver, H. O. (2020). Review of tool condition monitoring in machining and opportunities for deep learning. The International Journal of Advanced Manufacturing Technology, 1-22.en_US
dc.identifier.issn1433-3015-
dc.identifier.issn0268-3768-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/3832-
dc.identifier.urihttps://link.springer.com/article/10.1007/s00170-020-05449-w-
dc.description.abstractTool condition monitoring and machine tool diagnostics are performed using advanced sensors and computational intelligence to predict and avoid adverse conditions for cutting tools and machinery. Undesirable conditions during machining cause chatter, tool wear, and tool breakage, directly affecting the tool life and consequently the surface quality, dimensional accuracy of the machined parts, and tool costs. Tool condition monitoring is, therefore, extremely important for manufacturing efficiency and economics. Acoustic emission, vibration, power, and temperature sensors monitor the stability and efficiency of the machining process, collecting large amounts of data to detect tool wear, breakage, and chatter. Studies on monitoring the vibrations and acoustic emissions from machine tools have provided information and data regarding the detection of undesirable conditions. Herein, studies on tool condition monitoring are reviewed and classified. As Industry 4.0 penetrates all manufacturing sectors, the amount of manufacturing data generated has reached the level of big data, and classical artificial intelligence analyses are no longer adequate. Nevertheless, recent advances in deep learning methods have achieved revolutionary success in numerous industries. Deep multi-layer perceptron (DMLP), long-short-term memory (LSTM), convolutional neural network (CNN), and deep reinforcement learning (DRL) are among the most preferred methods of deep learning in recent years. As data size increases, these methods have shown promising performance improvement in prediction and learning, compared to classical artificial intelligence methods. This paper summarizes tool condition monitoring first, then presents the underlying theory of some of the most recent deep learning methods, and finally, attempts to identify new opportunities in tool condition monitoring, toward the realization of Industry 4.0.en_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofInternational Journal of Advanced Manufacturing Technologyen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectTool condition monitoringen_US
dc.subjectMachiningen_US
dc.subjectIndustry 4en_US
dc.subjectDeep multi-layer perceptronen_US
dc.subjectLong-short-term memoryen_US
dc.subjectConvolutional neural networken_US
dc.subjectReinforcement learningen_US
dc.titleReview of tool condition monitoring in machining and opportunities for deep learningen_US
dc.typeReviewen_US
dc.departmentFaculties, Faculty of Engineering, Department of Mechanical Engineeringen_US
dc.departmentFaculties, Faculty of Engineering, Department of Computer Engineeringen_US
dc.departmentFaculties, Faculty of Engineering, Department of Artificial Intelligence Engineeringen_US
dc.departmentFakülteler, Mühendislik Fakültesi, Makine Mühendisliği Bölümütr_TR
dc.departmentFakülteler, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümütr_TR
dc.departmentFakülteler, Mühendislik Fakültesi, Yapay Zeka Mühendisliği Bölümütr_TR
dc.identifier.volume109-
dc.identifier.startpage953-
dc.identifier.endpage974-
dc.relation.tubitakinfo:eu-repo/grantAgreement/TÜBİTAK/MAG/118M414en_US
dc.authorid0000-0002-4632-3505-
dc.authorid0000-0001-7998-5735-
dc.identifier.wosWOS:000547231800002en_US
dc.identifier.scopus2-s2.0-85087708373en_US
dc.institutionauthorÖzbayoglu, Ahmet Murat-
dc.institutionauthorÜnver, Hakkı Özgür-
dc.identifier.doi10.1007/s00170-020-05449-w-
dc.relation.publicationcategoryDiğeren_US
dc.identifier.scopusqualityQ1-
item.openairetypeReview-
item.fulltextNo Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.languageiso639-1en-
item.grantfulltextnone-
item.cerifentitytypePublications-
crisitem.author.dept02.1. Department of Artificial Intelligence Engineering-
crisitem.author.dept02.7. 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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