Towards specific cutting energy analysis in the machining of Inconel 601 alloy under sustainable cooling conditions

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dc.authoridGupta, Munish/0000-0002-0777-1559
dc.authoridChan, Choon Kit/0000-0001-7478-7334
dc.authoridKORKMAZ, Mehmet Erdi/0000-0002-0481-6002
dc.authoridSIVALINGAM, VINOTHKUMAR/0000-0002-6705-5933
dc.authoridRAMAN, JEYAGOPI/0009-0008-9104-5234
dc.contributor.authorKorkmaz, Mehmet Erdi
dc.contributor.authorGupta, Munish Kumar
dc.contributor.authorYilmaz, Hakan
dc.contributor.authorRoss, Nimel Sworna
dc.contributor.authorBoy, Mehmet
dc.contributor.authorSivalingam, Vinoth Kumar
dc.contributor.authorChan, Choon Kit
dc.date.accessioned2024-09-29T15:57:43Z
dc.date.available2024-09-29T15:57:43Z
dc.date.issued2023
dc.departmentKarabük Üniversitesien_US
dc.description.abstractCurrently, the research efforts on machining indices such as tool wear, surface roughness, power consumption etc. is well reported in literature, but energy analysis based on material removal methods and machine learning has received comparatively little attention. Therefore, the present work deals with the research efforts on simultaneous reduction of specific cutting energy in sustainable machining of Inconel 601 alloy with different machine learning models. The studies were conducted using dry, minimum quantity lubrication (MQL), nano-MQL, cryogenic, and hybrid cooling methods (cryo-nano-MQL). The specific cutting energy (SCE) values were calculated based on the data obtained from power consumption and material removal rate. Subsequently, the SCE data is employed to construct the crucial maps, which are then utilized in several sophisticated machine learning models, including Multiple Linear Regression, Lasso Regression, Bayesian Ridge Regression, and Voting Regressor, to facilitate the predictive modeling of outcomes. The findings of the study indicate that the Bayesian model exhibits a comparatively reduced error rate and a closely aligned R2 value when compared to other prediction models. Moreover, as a novelty, nanoparticles addition into hybrid cooling methods (cryo + nano + MQL) also showed better performance as well as 0.3 % less specific cutting energy than only cryo method which is previously used in former studies.en_US
dc.description.sponsorshipKarabuk university Project Coornadinaship [KBBAP-21-ABP-120]; Future for Young Scholars of Shandong University, China [31360082064026, 31360082164007]en_US
dc.description.sponsorshipThe author Hakan Yilmaz would like to acknowledge Karabuk university Project Coornadinaship with project no. KBUEBAP-21-ABP-120 for financial support. The author Vinoth Kumar Sivalingam also acknowledge the Future for Young Scholars of Shandong University, China [31360082064026, 31360082164007] for providing funding.en_US
dc.identifier.doi10.1016/j.jmrt.2023.10.192
dc.identifier.endpage4087en_US
dc.identifier.issn2238-7854
dc.identifier.issn2214-0697
dc.identifier.scopus2-s2.0-85176328030en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage4074en_US
dc.identifier.urihttps://doi.org/10.1016/j.jmrt.2023.10.192
dc.identifier.urihttps://hdl.handle.net/20.500.14619/4979
dc.identifier.volume27en_US
dc.identifier.wosWOS:001113013000001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofJournal of Materials Research and Technology-Jmr&Ten_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectArtificial intelligenceen_US
dc.subjectEnergy mapsen_US
dc.subjectIndustry 4.0en_US
dc.subjectMachine learningen_US
dc.subjectSustainable manufacturingen_US
dc.titleTowards specific cutting energy analysis in the machining of Inconel 601 alloy under sustainable cooling conditionsen_US
dc.typeArticleen_US

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