Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/11088
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dc.contributor.authorUnal, P.-
dc.contributor.authorTemel, S.-
dc.contributor.authorUmmak, E.-
dc.contributor.authorOzbayoglu, A.M.-
dc.date.accessioned2024-03-09T15:12:41Z-
dc.date.available2024-03-09T15:12:41Z-
dc.date.issued2023-
dc.identifier.isbn9798350316353-
dc.identifier.urihttps://doi.org/10.1109/FiCloud58648.2023.00046-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/11088-
dc.description10th International Conference on Future Internet of Things and Cloud, FiCloud 2023 -- 14 August 2023 through 16 August 2023 -- 196960en_US
dc.description.abstractIn this study, a cutting tool condition monitoring (TCM) platform for CNC machines to be used in metal part manufacturing is proposed to estimate the remaining useful life (RUL) of machine cutting tools. For this purpose, operational and situational data obtained from CNC machine and sensors will be analyzed with artificial intelligence algorithms, anomalies will be detected, and total equipment performance will be supported by using remaining life estimates.The innovative side of the system is the development of an artificial intelligence application that includes classification and regression methods with artificial neural networks. The use of RUL concept is relatively limited in the literature, but general interest by the industry is high. It will be among the first applications that machinery and machine cutting tools will be monitored and remaining useful life estimation will be made as an important contribution in the field.In the literature, examples that predict RUL of system are not included in the state monitoring of machine and machine cutting tools and in TCM applications. In our research, classification, and regression models and three different artificial neural network algorithms will be compared by using RUL estimation results that can be widely used and have a high impact potential, and corresponding studies will be carried out for the use of industry and increasing efficiency in the manufacturing sector. © 2023 IEEE.en_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofProceedings - 2023 International Conference on Future Internet of Things and Cloud, FiCloud 2023en_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectCNCen_US
dc.subjectcondition monitoringen_US
dc.subjectIndustry 4.0en_US
dc.subjectpredictive maintenanceen_US
dc.subjecttool wearen_US
dc.subjectComputer control systemsen_US
dc.subjectCondition monitoringen_US
dc.subjectIndustrial researchen_US
dc.subjectIndustry 4.0en_US
dc.subjectMetal cuttingen_US
dc.subjectNeural networksen_US
dc.subjectRegression analysisen_US
dc.subjectWear of materialsen_US
dc.subjectCNCen_US
dc.subjectCNC machineen_US
dc.subjectLife estimationen_US
dc.subjectMetal partsen_US
dc.subjectMonitoring platformen_US
dc.subjectPredictive maintenanceen_US
dc.subjectRemaining useful life predictionsen_US
dc.subjectRemaining useful livesen_US
dc.subjectTool condition monitoringen_US
dc.subjectTool wearen_US
dc.subjectCutting toolsen_US
dc.titleCondition Monitoring and Remaining Useful Life Prediction for Tool Wear in Cnc Machinesen_US
dc.typeConference Objecten_US
dc.departmentTOBB ETÜen_US
dc.identifier.startpage264en_US
dc.identifier.endpage269en_US
dc.identifier.scopus2-s2.0-85184997832en_US
dc.institutionauthorOzbayoglu, A.M.-
dc.identifier.doi10.1109/FiCloud58648.2023.00046-
dc.authorscopusid56396952700-
dc.authorscopusid58101634200-
dc.authorscopusid57350611900-
dc.authorscopusid57947593100-
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
item.openairetypeConference Object-
item.languageiso639-1en-
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
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