Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/9090
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dc.contributor.authorÜnal P.-
dc.contributor.authorDeveci B.U.-
dc.contributor.authorÖzbayoglu A.M.-
dc.date.accessioned2022-11-30T19:27:45Z-
dc.date.available2022-11-30T19:27:45Z-
dc.date.issued2022-
dc.identifier.isbn9.78303E+12-
dc.identifier.issn0302-9743-
dc.identifier.urihttps://doi.org/10.1007/978-3-031-14391-5_15-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/9090-
dc.description18th International Conference on Mobile Web and Intelligent Information Systems, MobiWIS 2022 -- 22 August 2022 through 24 August 2022 -- -- 281999en_US
dc.description.abstractTool wear prediction/monitoring of CNCs is crucial for improving manufacturing efficiency, guaranteeing product quality, and minimizing tool costs. As a computer-aided application, it has a significant role in the future and development of Industry 4.0. Sensors are the key piece of hardware used by data-driven enterprises to predict/monitor tool wear. The purpose of this study is to inform about the predominant types of sensors used for tool wear monitoring/prediction. This study serves as a resource for researchers and manufacturers by providing the recent trends in sensors for tool wear monitoring. Thus, it may help reduce the time spent on sensor selection. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.en_US
dc.language.isoenen_US
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.relation.ispartofLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)en_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectAccelerometeren_US
dc.subjectAcoustic emissionen_US
dc.subjectCurrent sensoren_US
dc.subjectDynamometeren_US
dc.subjectIndustry 4.0en_US
dc.subjectMicrophoneen_US
dc.subjectSensorsen_US
dc.subjectAcoustic emission testingen_US
dc.subjectCutting toolsen_US
dc.subjectIndustry 4.0en_US
dc.subjectWear of materialsen_US
dc.subjectAcoustic-emissionsen_US
dc.subjectComputer-aideden_US
dc.subjectCurrent sensorsen_US
dc.subjectData drivenen_US
dc.subjectManufacturing efficiencyen_US
dc.subjectProducts qualityen_US
dc.subjectRecent trendsen_US
dc.subjectTool wearen_US
dc.subjectTool wear monitoringen_US
dc.subjectWear predictionen_US
dc.subjectForecastingen_US
dc.titleA Review: Sensors Used in Tool Wear Monitoring and Predictionen_US
dc.typeConference Objecten_US
dc.identifier.volume13475 LNCSen_US
dc.identifier.startpage193en_US
dc.identifier.endpage205en_US
dc.identifier.wosWOS:000870672500015en_US
dc.identifier.scopus2-s2.0-85136935849en_US
dc.institutionauthorÖzbayoglu, Ahmet Murat-
dc.identifier.doi10.1007/978-3-031-14391-5_15-
dc.authorscopusid56396952700-
dc.authorscopusid57350944900-
dc.authorscopusid6505999525-
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.identifier.scopusqualityQ3-
dc.ozel2022v3_Editen_US
item.cerifentitytypePublications-
item.fulltextNo Fulltext-
item.grantfulltextnone-
item.openairetypeConference Object-
item.languageiso639-1en-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
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