Determination of flow distance of the fluid metal due to fluidity in ductile iron casting by artificial neural networks approach

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dc.contributor.authorTeke, Cagatay
dc.date.accessioned2024-10-04T18:49:27Z
dc.date.available2024-10-04T18:49:27Z
dc.date.issued2022
dc.departmentBayburt Üniversitesien_US
dc.description.abstractDuctile irons (DIs) have properties such as high strength, ductility, and toughness, as well as a low degree of melting, good fluidity, and good machining. Having these characteristics make them the most preferred among cast irons. The combination of excellent properties, especially in DI castings with a thin section, make it an alternative for steel casting and forging. But in the manufacture of thin-section parts, fluidity characteristics need to be improved and the liquid metal must fill the mold completely. The fluidity of liquid metal is influenced by many factors depending on the casting processes such as material and mold properties, casting temperature, inoculation, globalization, and grain refinement. In this study, an artificial neural network (ANN) model has been developed that allows for determining the flow distance of the liquid metal in the sand mold casting method under changing casting conditions of DI. Thus, the flow distance was estimated depending on the cross-sectional thickness during the sand casting under changing casting conditions. The experimental parameters were determined as casting temperature, liquid metal metallurgy quality, cross-sectional thickness, and filling time. Filling modeling was performed with FlowCast software. When the results were examined, it was seen that the developed ANN model has high success in predicting the flow distances of the liquid metal under different casting conditions. The calculated coefficient of determination (R (2)) value of 0.986 confirms the high prediction performance of the model.en_US
dc.identifier.doi10.1515/chem-2022-0210
dc.identifier.endpage1028en_US
dc.identifier.issn2391-5420
dc.identifier.issue1en_US
dc.identifier.scopus2-s2.0-85140582727en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage1019en_US
dc.identifier.urihttps://doi.org/10.1515/chem-2022-0210
dc.identifier.urihttp://hdl.handle.net/20.500.12403/3152
dc.identifier.volume20en_US
dc.identifier.wosWOS:000864840300001en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherDe Gruyter Poland Sp Z O Oen_US
dc.relation.ispartofOpen Chemistryen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectfluidityen_US
dc.subjectductile ironen_US
dc.subjectartificial neural networken_US
dc.subjectpredictionen_US
dc.subjectcasting simulationen_US
dc.titleDetermination of flow distance of the fluid metal due to fluidity in ductile iron casting by artificial neural networks approachen_US
dc.typeArticleen_US

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