Prediction of Yoshida Uemori model parameters by the bees algorithm and Genetic Algorithm for 5xxx series aluminium alloys

dc.contributor.authorKorkmaz, Habip Gökay
dc.contributor.authorToros, Serkan
dc.contributor.authorKalyoncu, Mete
dc.date.accessioned2024-11-07T13:16:54Z
dc.date.available2024-11-07T13:16:54Z
dc.date.issued2021
dc.departmentNiğde Ömer Halisdemir Üniversitesi
dc.description.abstractIn sheet metal forming processes, springback is a very important issue in the view of the excellent quality design. Several mathematical models have been developed to estimate the springback more accurately, including various material parameters. In this study, the model parameters of Yoshida-Uemori two surface plasticity model, which can well predict the springback for different loading conditions, have been determined using The Bees Algorithm and Genetic Algorithm which are frequently used recently for optimization of nonlinear problems. In addition, the performances of the algorithms have been determined for the different frequency of experimental data, dense-sparse, sparse-dense, dense-dense and sparse-sparse for elastic and plastic regions. According to the results, although the determined material parameters have different values, the fitting performances are found similar for both The Bees Algorithm and Genetic Algorithm. However, in the view of the data frequency, the more appropriate results are obtained from the dense-dense data set (Case 3).
dc.identifier.doi10.28948/ngumuh.895920
dc.identifier.endpage823
dc.identifier.issn2564-6605
dc.identifier.issue2
dc.identifier.startpage815
dc.identifier.trdizinid468917
dc.identifier.urihttps://doi.org/10.28948/ngumuh.895920
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay/468917
dc.identifier.urihttps://hdl.handle.net/11480/12649
dc.identifier.volume10
dc.indekslendigikaynakTR-Dizin
dc.language.isoen
dc.relation.ispartofNiğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241107
dc.subjectMalzeme Bilimleri
dc.subjectÖzellik ve Test
dc.subjectMetalürji Mühendisliği
dc.titlePrediction of Yoshida Uemori model parameters by the bees algorithm and Genetic Algorithm for 5xxx series aluminium alloys
dc.typeArticle

Dosyalar