The prediction of surface roughness and tool vibration by using metaheuristic-based ANFIS during dry turning of Al alloy (AA6013)

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dc.contributor.authorGuvenc, Mehmet Ali
dc.contributor.authorBilgic, Hasan Huseyin
dc.contributor.authorCakir, Mustafa
dc.contributor.authorMistikoglu, Selcuk
dc.date.accessioned2024-02-23T14:00:10Z
dc.date.available2024-02-23T14:00:10Z
dc.date.issued2022
dc.departmentNEÜen_US
dc.description.abstractIn this article, the adaptive neuro-based fuzzy inference system (ANFIS) model is developed to estimate the surface roughness (Ra) and tool vibrations (Acc) of AA6013 aluminum alloy during dry turning. Turning experiments were carried out with seven different cutting speeds, five different feed rates and seven different depth of cuts. These three different cutting parameters were tested with each other in different variations. ANFIS model is optimized using the genetic algorithm (GA), particle swarm optimization (PSO) and ant colony optimization. Performance of the developed model is compared with that of multi-linear regression model, which is one of the conventional prediction approaches. At the end of the study, it is revealed that the GA-ANFIS with an R-value of 0.946 is seen as the best model among the proposed approaches in the estimation of Acc. The PSO-ANFIS with an R-value of 0.916 is seen as the best model among the proposed approaches in the estimation of Ra. GA-ANFIS model for Acc prediction and PSO-ANFIS model for Ra prediction are the best approaches among the models discussed in the study. Moreover, the relationship between Acc and Ra values was examined and an empirical model was proposed.en_US
dc.description.sponsorshipIskenderun Technical University; Iskenderun Technical University, Faculty of Engineering and Natural Sciencesen_US
dc.description.sponsorshipThis work was supported by the Iskenderun Technical University scope of PhD Thesis of Mehmet Ali GUVENC. We would like to thank Iskenderun Technical University, Faculty of Engineering and Natural Sciences, for their support. Finally, we would like to thank Konya Technical University and Prof. Dr. Mete Kalyoncu for allowing us to use the laboratory facilities.Yen_US
dc.identifier.doi10.1007/s40430-022-03798-z
dc.identifier.issn1678-5878
dc.identifier.issn1806-3691
dc.identifier.issue10en_US
dc.identifier.scopus2-s2.0-85138621510en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.1007/s40430-022-03798-z
dc.identifier.urihttps://hdl.handle.net/20.500.12452/11481
dc.identifier.volume44en_US
dc.identifier.wosWOS:000858865600001en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer Heidelbergen_US
dc.relation.ispartofJournal Of The Brazilian Society Of Mechanical Sciences And Engineeringen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectAdaptive Neuro-Based Fuzzy Inference Systemen_US
dc.subjectTurningen_US
dc.subjectSurface Roughnessen_US
dc.subjectTool Vibrationen_US
dc.subjectPso-Anfisen_US
dc.subjectGa-Anfisen_US
dc.subjectAco-Anfisen_US
dc.subjectMlrmen_US
dc.titleThe prediction of surface roughness and tool vibration by using metaheuristic-based ANFIS during dry turning of Al alloy (AA6013)en_US
dc.typeArticleen_US

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