Sustainable Machining of Nimonic 80A: Machine Learning-Driven Optimization of Energy Consumption Under Environmentally Conscious Cooling Strategies

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dc.contributor.authorDanish, Mohd
dc.contributor.authorSarikaya, Murat
dc.date.accessioned2026-04-25T14:19:54Z
dc.date.available2026-04-25T14:19:54Z
dc.date.issued2026
dc.departmentSinop Üniversitesi
dc.description.abstractMachining advanced materials like Nimonic 80A in a way that is both environmentally friendly and energy efficient is becoming more important around the world. However, achieving an acceptable level of machining performance without harming the environment remains challenging. The present study is focused on resolving this issue. Machine Learning (ML) was employed to predict and optimize cutting power and specific cutting energy (SCE). Four novel sustainable cooling and lubrication (C/L) environments were studied which are dry, minimum quantity lubrication (MQL), cryogenic CO2, and hybrid CO2 + MQL configuration. The main contribution of the study is the systematic comparison of ML techniques such as support vector machine (SVM), random forest (RF), and multi-layer perceptron (MLP) for getting the prediction and optimization of the cutting power and SCE among different C/L environments. Among all C/L environments, the hybrid (CO2 + MQL) environment produced the best results, significantly reducing power consumption and SCE. Under the best conditions, the hybrid method used about 47% less power and about 80% less SCE than dry machining. ML analysis showed the superior predictive capability of the MLP model, which achieved the lowest error rates and highest coefficients (R = 0.9996 for power; R = 0.9873 for SCE). These results demonstrate the strong potential of combining hybrid cooling with intelligent ML prediction for sustainable and energy-efficient machining of Nimonic 80A.
dc.description.sponsorshipUniversity of Jeddah [UJ-24-DR-20742-1]; Sinop University
dc.description.sponsorshipOpen access funding provided by the Scientific and Technological Research Council of Turkiye (TUB & Idot;TAK).
dc.identifier.doi10.1007/s13369-025-10947-8
dc.identifier.issn2193-567X
dc.identifier.issn2191-4281
dc.identifier.scopus2-s2.0-105027779757
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1007/s13369-025-10947-8
dc.identifier.urihttps://hdl.handle.net/11486/8234
dc.identifier.wosWOS:001663472900001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer Heidelberg
dc.relation.ispartofArabian Journal for Science and Engineering
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20260420
dc.subjectSustainable machining
dc.subjectEnergy efficiency
dc.subjectNimonic 80A
dc.subjectAdvanced cooling strategies
dc.subjectMachine learning
dc.subjectSpecific cutting energy
dc.subjectCryogenic CO2
dc.titleSustainable Machining of Nimonic 80A: Machine Learning-Driven Optimization of Energy Consumption Under Environmentally Conscious Cooling Strategies
dc.typeArticle

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