Prediction and classification of tool wear and its state in sustainable machining of Bohler steel with different machine learning models

Basit Öğe Kaydı
dc.authoridVashishtha, Govind/0000-0002-5160-9647
dc.authoridGupta, Munish/0000-0002-0777-1559
dc.contributor.authorKorkmaz, Mehmet Erdi
dc.contributor.authorGupta, Munish Kumar
dc.contributor.authorKuntoglu, Mustafa
dc.contributor.authorPatange, Abhishek D.
dc.contributor.authorRoss, Nimel Sworna
dc.contributor.authorYilmaz, Hakan
dc.contributor.authorChauhan, Sumika
dc.date.accessioned2024-09-29T15:57:56Z
dc.date.available2024-09-29T15:57:56Z
dc.date.issued2023
dc.departmentKarabük Üniversitesien_US
dc.description.abstractMachine learning has numerous advantages, especially in the rapid digitization of the manufacturing industry that combines data from manufacturing processes and quality measures. Predictive quality allows manufacturers to make informed predictions about the quality of their products by analyzing data gathered during production. The quality of the machining, the total cost and the computation time need to be improved using contemporary production processes. With this concern, a series of experiments were carried out on Bohler steel both in dry, Minimum Quantity Lubrication (MQL) and nano-MQL conditions in varying quantities to explore the tool wear. In comparison to dry conditions, the utilization of MQL in machining processes demonstrates significantly enhanced efficacy in mitigating flank wear. The reduction in flank wear ranges from around 5% to 20% to 25%, contingent upon the application of MQL on the flank face, rake face, or both faces simultaneously. After that, the results of the tests were evaluated with the models of machine learning (ML) to determine which environment was optimal for cutting under both real and artificial circumstances.en_US
dc.description.sponsorshipOpole University of Technology [269/23]; Karabuek University [KBUEBAP-21-ABP-120]en_US
dc.description.sponsorshipThe corresponding author Munsh Kumar Gupta acknowledges the financially support provided by the Opole University of Technology as part of the GRAS project no. 269/23. The author Hakan Y & imath;lmaz would like to thanks Karabuek University Scientific Research Coordinatorship with the Project number of KBUEBAP-21-ABP-120.en_US
dc.identifier.doi10.1016/j.measurement.2023.113825
dc.identifier.issn0263-2241
dc.identifier.issn1873-412X
dc.identifier.scopus2-s2.0-85176937284en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.measurement.2023.113825
dc.identifier.urihttps://hdl.handle.net/20.500.14619/5107
dc.identifier.volume223en_US
dc.identifier.wosWOS:001149582600001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevier Sci Ltden_US
dc.relation.ispartofMeasurementen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectMachine learningen_US
dc.subjectSupport vector regressionen_US
dc.subjectDecision treeen_US
dc.subjectTool condition monitoringen_US
dc.subjectMachiningen_US
dc.titlePrediction and classification of tool wear and its state in sustainable machining of Bohler steel with different machine learning modelsen_US
dc.typeArticleen_US

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu