Key initiatives to improve the machining characteristics of Inconel-718 alloy: Experimental analysis and optimization

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dc.authoridSarikaya, Murat/0000-0001-6100-0731
dc.authoridAhmed, Anas/0000-0003-1179-9092
dc.authoridDanish, Mohd/0000-0001-7505-0983
dc.authoridRubaiee, Saeed/0000-0002-4433-5529
dc.authoridGupta, Munish/0000-0002-0777-1559
dc.authoridKORKMAZ, Mehmet Erdi/0000-0002-0481-6002
dc.contributor.authorRubaiee, Saeed
dc.contributor.authorDanish, Mohd
dc.contributor.authorGupta, Munish Kumar
dc.contributor.authorAhmed, Anas
dc.contributor.authorYahya, Syed Mohd
dc.contributor.authorYildirim, Mehmet Bayram
dc.contributor.authorSarikaya, Murat
dc.date.accessioned2024-09-29T15:57:43Z
dc.date.available2024-09-29T15:57:43Z
dc.date.issued2022
dc.departmentKarabük Üniversitesien_US
dc.description.abstractInconel 718 is a heat-resistant Ni-based superalloy widely used, particularly, in aircraft and aero-engineering applications. It has poor machinability due to its unique thermal and mechanical properties. For this reason, studies have been carried out from past to present to improve the machinability of Nickel-based (Ni) alloys. Further improvement can be achieved by applying hybrid multi-objective optimization strategies to ensure that cutting parameters and cooling/lubrication strategies are also adjusted effectively. That is why, in this research, the machinability of Inconel 718 is optimized under various sustainable lubricating environments i.e., dry medium, minimum quantity lubrication (MQL), nano-MQL, and cryogenic conditions at different machining parameters during end-milling process. Subsequently, the analysis of variance (ANOVA) approach was implanted to apprehend the impact of each machining parameter. Finally, to optimize machining en-vironments, two advanced optimization algorithms (non-dominated sorting genetic algo-rithm II (NSGA-II) and the Teaching-learning-based optimization (TLBO) approach) were introduced. As a result, both methods have demonstrated remarkable efficiency in ma-chine response prediction. Both methodologies demonstrate that a cutting speed of 90 m/ min, feed rate of 0.05 mm/rev, and CO2 snow are the optimal circumstances for minimizing machining responses during milling of Inconel 718. (C) 2022 The Author(s). Published by Elsevier B.V.en_US
dc.identifier.doi10.1016/j.jmrt.2022.10.060
dc.identifier.endpage2720en_US
dc.identifier.issn2238-7854
dc.identifier.issn2214-0697
dc.identifier.scopus2-s2.0-85144822591en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage2704en_US
dc.identifier.urihttps://doi.org/10.1016/j.jmrt.2022.10.060
dc.identifier.urihttps://hdl.handle.net/20.500.14619/4978
dc.identifier.volume21en_US
dc.identifier.wosWOS:000883064900008en_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.subjectInconel 718en_US
dc.subjectCooling/lubrication strategiesen_US
dc.subjectEnd millingen_US
dc.subjectAdvanced optimization approachesen_US
dc.subjectNSGA-II and TLBOen_US
dc.titleKey initiatives to improve the machining characteristics of Inconel-718 alloy: Experimental analysis and optimizationen_US
dc.typeArticleen_US

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