A novel approach of tool condition monitoring in sustainable machining of Ni alloy with transfer learning models

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dc.authoridGupta, Munish/0000-0002-0777-1559
dc.authoridKORKMAZ, Mehmet Erdi/0000-0002-0481-6002
dc.authoridShibi, Sherin/0000-0002-7942-2438
dc.contributor.authorRoss, Nimel Sworna
dc.contributor.authorSheeba, Paul T. T.
dc.contributor.authorShibi, C. Sherin
dc.contributor.authorGupta, Munish Kumar
dc.contributor.authorKorkmaz, Mehmet Erdi
dc.contributor.authorSharma, Vishal S.
dc.date.accessioned2024-09-29T15:51:14Z
dc.date.available2024-09-29T15:51:14Z
dc.date.issued2024
dc.departmentKarabük Üniversitesien_US
dc.description.abstractCutting tool condition is crucial in metal cutting. In-process tool failures significantly influences the surface roughness, power consumption, and process endurance. Industries are interested in supervisory systems that anticipate the health of the tool. A methodology that utilizes the information to predict problems and to avoid failures must be embraced. In recent years, several machine learning-based predictive modelling strategies for estimating tool wear have been emerged. However, due to intricate tool wear mechanisms, doing so with limited datasets confronts difficulties under varying operating conditions. This article proposes the use of transfer learning technology to detect tool wear, especially flank wear under distinct cutting environments (dry, flood, MQL and cryogenic). In this study, the state of the cutting tool was determined using the pre-trained networks like AlexNet, VGG-16, ResNet, MobileNet, and Inception-V3. The best-performing network was recommended for tool condition monitoring, considering the effects of hyperparameters such as batch size, learning rate, solver, and train-test split ratio. In light of this, the recommended methodology may prove to be highly helpful for classifying and suggesting the suitable cutting conditions, especially under limited data situation. The transfer learning model with Inception-V3 is extremely useful for intelligent machining applications.en_US
dc.description.sponsorshipNorwegian Financial Mechanism [2020/37/K/ST8/02795]; Polish National Agency For Academic Exchange (NAWA) [PPN/ULM/2020/1/00121]en_US
dc.description.sponsorshipThe research leading to these results has received funding from the Norwegian Financial Mechanism 2014-2021, Project Contract No 2020/37/K/ST8/02795. The authors also acknowledge the Polish National Agency For Academic Exchange (NAWA) No. PPN/ULM/2020/1/00121 for financial support.en_US
dc.identifier.doi10.1007/s10845-023-02074-8
dc.identifier.endpage775en_US
dc.identifier.issn0956-5515
dc.identifier.issn1572-8145
dc.identifier.issue2en_US
dc.identifier.scopus2-s2.0-85146154389en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage757en_US
dc.identifier.urihttps://doi.org/10.1007/s10845-023-02074-8
dc.identifier.urihttps://hdl.handle.net/20.500.14619/3964
dc.identifier.volume35en_US
dc.identifier.wosWOS:000912966400001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofJournal of Intelligent Manufacturingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectArtificial intelligenceen_US
dc.subjectImage processingen_US
dc.subjectTransfer learningen_US
dc.subjectTool wearen_US
dc.subjectTool condition monitoringen_US
dc.titleA novel approach of tool condition monitoring in sustainable machining of Ni alloy with transfer learning modelsen_US
dc.typeArticleen_US

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