Prediction of power consumption and its signals in sustainable turning of PH13-8Mo steel with different machine learning models

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dc.authoridGupta, Munish/0000-0002-0777-1559
dc.contributor.authorYurtkuran, Hakan
dc.contributor.authorKorkmaz, Mehmet Erdi
dc.contributor.authorGupta, Munish Kumar
dc.contributor.authorYilmaz, Hakan
dc.contributor.authorGunay, Mustafa
dc.contributor.authorVashishtha, Govind
dc.date.accessioned2024-09-29T15:51:01Z
dc.date.available2024-09-29T15:51:01Z
dc.date.issued2024
dc.departmentKarabük Üniversitesien_US
dc.description.abstractDue to extensive distribution and huge demand of energy efficient processes, the energy-saving of machining processes draws more and more attention, and a significant variety of methods have evolved to prognosis or optimise the energy consumption in machining operations. Similarly, the estimation of power consumption-cutting conditions relationships is of great importance for optimizing processing costs and for cleaner machining. Compared to traditional methods, machine learning (ML) approach is one of the effective analysis options to model machinability indicators such as cutting force, tool wear, power consumption and surface quality. In this study, PH13-8Mo stainless steel was machined with coated carbide inserts using primarily Dry, MQL, nano-Graphene + MQL, nano-hBN + MQL, Cryo, Cryo + MQL cutting environments. Power consumption and its signals during milling were measured and different machine learning models were applied to estimate the Pc. To develop the Pc model based on the ML algorithm, 70% of the power consumption data is reserved for training and 30% for testing. In all cutting environments, power consumption increased by an average of 3.14% as feed speed increased. The reduction in Pc compared to the dry cutting was calculated as an average of 2.2%, 3.17%, 2.57%, 4.88% and 5.45% for MQL, nano-Graphen + MQL, nano-hBN + MQL, Cryo, Cryo + MQL, respectively. It is seen that the developed prediction model can reflect the power consumption-parameter relationships at high accuracy.en_US
dc.identifier.doi10.1007/s00170-024-13867-3
dc.identifier.endpage2188en_US
dc.identifier.issn0268-3768
dc.identifier.issn1433-3015
dc.identifier.issue5-6en_US
dc.identifier.scopus2-s2.0-85195306150en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage2171en_US
dc.identifier.urihttps://doi.org/10.1007/s00170-024-13867-3
dc.identifier.urihttps://hdl.handle.net/20.500.14619/3827
dc.identifier.volume133en_US
dc.identifier.wosWOS:001242156400001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer London Ltden_US
dc.relation.ispartofInternational Journal of Advanced Manufacturing Technologyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectMachine learningen_US
dc.subjectPower consumptionen_US
dc.subjectSustainable machiningen_US
dc.subjectPH13-8Mo steelen_US
dc.titlePrediction of power consumption and its signals in sustainable turning of PH13-8Mo steel with different machine learning modelsen_US
dc.typeArticleen_US

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