Flow curve prediction of Al-Mg alloys under warm forming conditions at various strain rates by ANN

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dc.authorid0000-0001-9517-7957
dc.contributor.authorToros, Serkan
dc.contributor.authorOzturk, Fahrettin
dc.date.accessioned2019-08-01T13:38:39Z
dc.date.available2019-08-01T13:38:39Z
dc.date.issued2011
dc.departmentNiğde ÖHÜ
dc.description.abstractThis paper describes an approach based on artificial neural network (ANN) to identify the material flow curves of strain hardened 5083-H111 and 5754-O Al-Mg alloys at the temperature ranges from room temperature (RT) to 300 degrees C and a strain rate of 0.0016-0.16 s(-1). The tensile tests were performed to determine the material responses at various temperatures and strain rates. An ANN model was developed to predict the flow curves of the materials in terms of experimental data. The input parameters of the model are strain rate, temperature, and strain while tensile flow stress is the output. A three layer feed-forward network was trained with BFGS (Broyden, Fletcher, Goldfarb, and Shanno) algorithm. The amount of the neurons in the hidden layer was determined by determining of the root mean square error (RMSE) values for each material. Results reveal that the predicted values in the ANN model are in very good agreement with the experimental data. The ANN model, described in this paper, is an efficient quantitative tool to evaluate and predict the deformation behavior of 5083-H111 and 5754-O Al-Mg alloys for tensile test at prescribed deformation conditions. (C) 2010 Elsevier B.V. All rights reserved.
dc.description.sponsorshipScientific and Technological Research Council of Turkey (TUBITAK) [106M058]
dc.description.sponsorshipThis work is supported by The Scientific and Technological Research Council of Turkey (TUBITAK). Project number: 106M058, Title: "Experimental and Theoretical Investigations of The Effects of Temperature and Deformation Speed on Formability". TUBITAK support is profoundly acknowledged.
dc.identifier.doi10.1016/j.asoc.2010.06.004
dc.identifier.endpage1898
dc.identifier.issn1568-4946
dc.identifier.issn1872-9681
dc.identifier.issue2
dc.identifier.scopus2-s2.0-78751628838
dc.identifier.scopusqualityQ1
dc.identifier.startpage1891
dc.identifier.urihttps://dx.doi.org/10.1016/j.asoc.2010.06.004
dc.identifier.urihttps://hdl.handle.net/11480/4743
dc.identifier.volume11
dc.identifier.wosWOS:000286373200042
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthor[0-Belirlenecek]
dc.language.isoen
dc.publisherELSEVIER SCIENCE BV
dc.relation.ispartofAPPLIED SOFT COMPUTING
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectAl-Mg alloys
dc.subject5083
dc.subject5754
dc.subjectFlow curves
dc.subjectModeling
dc.subjectArtificial neural network
dc.subjectANN
dc.titleFlow curve prediction of Al-Mg alloys under warm forming conditions at various strain rates by ANN
dc.typeArticle

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