Prediction of critical fraction of solid in low-pressure die casting of aluminum alloys using artificial neural network

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dc.contributor.authorTeke, C.
dc.contributor.authorColak, M.
dc.contributor.authorKiraz, A.
dc.contributor.authorIpek, M.
dc.date.accessioned2024-10-04T18:52:40Z
dc.date.available2024-10-04T18:52:40Z
dc.date.issued2019
dc.departmentBayburt Üniversitesien_US
dc.description.abstractCasting simulation programs are computer programs that digitally model the casting of an alloy in the sand, shell, or permanent mold and, then, the cooling and solidification processes. However, obtaining consistent results out of casting modeling depends on the incorporation of many accurate parameters and boundary conditions. Critical Fraction of Solid (CFS), which is one of the most important of these parameters, is defined as the point where solid dendrites do not allow any flow of the liquid metal in the mushy zone. Since the CFS value varies based on many factors, inconsistent results can be experienced in the modeling applications. In this study, the CFS value obtained during the solidification of various commercial aluminum alloys' casting process, carried out using low-pressure die casting method, is predicted by using Artificial Neural Network (ANN) method based on alloy type, grain refiner and modifier additions, initial mold temperature, and pressure level parameters. In the scope of the study, 162 experiments are conducted. The results obtained from the low-pressure die casting experiments using a special model designed for the study are validated by using SOLIDCast casting simulation. The CFS values are obtained in this validation range of 33% to 61%. These CFS values are used in the development of ANN models. Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Mean Square Error (MSE) are used to assess the prediction accuracy of the ANN models. Calculated values of MAE, MAPE, and MSE are 0.0188, 7.06%, and 0.0006, respectively. The results show that the proposed ANN model predicts a CFS value with high accuracy. (C) 2019 Sharif University of Technology. All rights reserved.en_US
dc.identifier.doi10.24200/sci.2019.50819.1881
dc.identifier.endpage3312en_US
dc.identifier.issn1026-3098
dc.identifier.issue6en_US
dc.identifier.scopus2-s2.0-85099399832en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage3304en_US
dc.identifier.urihttps://doi.org/10.24200/sci.2019.50819.1881
dc.identifier.urihttp://hdl.handle.net/20.500.12403/3611
dc.identifier.volume26en_US
dc.identifier.wosWOS:000514808700007en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSharif Univ Technologyen_US
dc.relation.ispartofScientia Iranicaen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectCritical fraction of soliden_US
dc.subjectArtificial neural networken_US
dc.subjectLow pressure die castingen_US
dc.subjectCasting simulationen_US
dc.titlePrediction of critical fraction of solid in low-pressure die casting of aluminum alloys using artificial neural networken_US
dc.typeArticleen_US

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