Carbon emissions and overall sustainability assessment in eco-friendly machining of Monel-400 alloy

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dc.authoridKORKMAZ, Mehmet Erdi/0000-0002-0481-6002
dc.authoridRai, Ritu/0009-0007-7322-7344
dc.authoridGupta, Munish/0000-0002-0777-1559
dc.authoridM, BELSAM JEBA ANANTH/0000-0003-4799-018X
dc.authoridM, Ganesh/0000-0003-4517-1906
dc.contributor.authorRoss, Nimel Sworna
dc.contributor.authorRai, Ritu
dc.contributor.authorAnanth, M. B. J.
dc.contributor.authorSrinivasan, D.
dc.contributor.authorGanesh, M.
dc.contributor.authorGupta, Munish Kumar
dc.contributor.authorKorkmaz, Mehmet Erdi
dc.date.accessioned2024-09-29T16:00:48Z
dc.date.available2024-09-29T16:00:48Z
dc.date.issued2023
dc.departmentKarabük Üniversitesien_US
dc.description.abstractWith increasing regulations about global warming, environmental pollution, and climate change, reducing carbon emissions from energy-intensive industrial activities routes to sustainable production. Because of its robust thermo-physical qualities at elevated temperatures, Monel 400 alloy is a renowned material for employment in modern aviation, medical tools, and prosthetic parts. Though, its structural stability imparts its low thermal conductivity that causes heat accumulation at the tool-workpiece contact during machining, resulting in tool cutting-edge damage. Many bio-based cutting fluids have been already tried to curtail heat generation and environmental footprints to progress overall machinability. In this endeavor, the effectiveness of dry, minimum quantity lubrication (MQL), cryogenic carbon dioxide (CO2) and Nano based MQL (N-MQL) are evaluated in terms of important sustainability indicator Carbon emission (CE). Multi-walled carbon Nano-tubes (MWCNT) in MQL oil limit the friction at the contact region which in turn reduces the power consumption. The highest CE value was found under a dry (0.0051 Kg-CO2) cutting environment and the lowest with N-MQL (0.0014 Kg-CO2). The sustainability assessment was done for CE with the help of Machine learning (ML) tech-niques like Decision tree (DT), Naive Bayes, Random Forest (RF), and Support Vector Machine (SVM). Finally, when the CE levels are discretized while considering industrial needs, SVM paired with the Synthetic Minority Over-sampling approach (SMOTE) demonstrated an accuracy of almost around 100%.en_US
dc.identifier.doi10.1016/j.susmat.2023.e00675
dc.identifier.issn2214-9937
dc.identifier.scopus2-s2.0-85166189474en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.susmat.2023.e00675
dc.identifier.urihttps://hdl.handle.net/20.500.14619/5365
dc.identifier.volume37en_US
dc.identifier.wosWOS:001051959200001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofSustainable Materials and Technologiesen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectIndustry 4en_US
dc.subject0en_US
dc.subjectResource savingsen_US
dc.subjectMetal cuttingen_US
dc.subjectMachine learningen_US
dc.subjectSustainable manufacturingen_US
dc.titleCarbon emissions and overall sustainability assessment in eco-friendly machining of Monel-400 alloyen_US
dc.typeArticleen_US

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