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

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dc.authoridYILDIRIM, MEHMET/0000-0003-4768-4537
dc.authoridDanish, Mohd/0000-0001-7505-0983
dc.authoridKORKMAZ, Mehmet Erdi/0000-0002-0481-6002
dc.authoridSarikaya, Murat/0000-0001-6100-0731
dc.authoridAhmed, Anas/0000-0003-1179-9092
dc.authoridYildirim, Mehmet Bayram/0000-0003-2900-3769
dc.authoridRubaiee, Saeed/0000-0002-4433-5529
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.accessioned2025-03-23T19:41:01Z
dc.date.available2025-03-23T19:41:01Z
dc.date.issued2022
dc.departmentSinop Üniversitesi
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.
dc.identifier.doi10.1016/j.jmrt.2022.10.060
dc.identifier.endpage2720
dc.identifier.issn2238-7854
dc.identifier.issn2214-0697
dc.identifier.scopus2-s2.0-85144822591
dc.identifier.scopusqualityQ1
dc.identifier.startpage2704
dc.identifier.urihttps://doi.org/10.1016/j.jmrt.2022.10.060
dc.identifier.urihttps://hdl.handle.net/11486/6494
dc.identifier.volume21
dc.identifier.wosWOS:000883064900008
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier
dc.relation.ispartofJournal of Materials Research and Technology-Jmr&T
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20250323
dc.subjectInconel 718
dc.subjectCooling/lubrication strategies
dc.subjectEnd milling
dc.subjectAdvanced optimization approaches
dc.subjectNSGA-II and TLBO
dc.titleKey initiatives to improve the machining characteristics of Inconel-718 alloy: Experimental analysis and optimization
dc.typeArticle

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