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dc.contributor.authorRojíček, Jaroslav
dc.contributor.authorČermák, Martin
dc.contributor.authorHalama, Radim
dc.contributor.authorPaška, Zbyněk
dc.contributor.authorVaško, Milan
dc.date.accessioned2021-10-21T11:06:08Z
dc.date.available2021-10-21T11:06:08Z
dc.date.issued2021
dc.identifier.citationMathematics and Computers in Simulation. 2021, vol. 189, special issue, p. 339-367.cs
dc.identifier.issn0378-4754
dc.identifier.issn1872-7166
dc.identifier.urihttp://hdl.handle.net/10084/145340
dc.description.abstractA methodology for calibration and validation of material models using more simultaneous experiments is presented. The procedure contains the selection of suitable parameters for identification using a sensitivity analysis, parameters identification, analysis of usability of the material model, and validation of the material model. Three elastoplastic models are evaluated in this study: the bilinear isotropic hardening model with Von Mises yield condition, the bilinear isotropic hardening model with the Hill anisotropic yield condition, and the nonlinear isotropic hardening model with Hill anisotropic yield condition. Five proportional as well as nonproportional monotonic loading experiments conducted on aluminium alloy 2124-T851 are used for the presentation of the inverse approach application, whereas four of them are the source of identification. The validation of calibrated material models is done based on the strain field measurement realised by the Digital Image Correlation method. The nonlinear isotropic hardening rule together with Hill yield condition brings the best description of stress-strain behaviour of the material under investigation.cs
dc.language.isoencs
dc.publisherElseviercs
dc.relation.ispartofseriesMathematics and Computers in Simulationcs
dc.relation.urihttps://doi.org/10.1016/j.matcom.2021.04.007cs
dc.rights© 2021 International Association for Mathematics and Computers in Simulation (IMACS). Published by Elsevier B.V. All rights reserved.cs
dc.subjectinverse identificationcs
dc.subjectfinite element methodcs
dc.subjectexperimentscs
dc.subjectdigital image correlation methodcs
dc.subjectsensitivity analysiscs
dc.titleMaterial model identification from set of experiments and validation by DICcs
dc.typearticlecs
dc.identifier.doi10.1016/j.matcom.2021.04.007
dc.type.statusPeer-reviewedcs
dc.description.sourceWeb of Sciencecs
dc.description.volume189cs
dc.description.lastpage367cs
dc.description.firstpage339cs
dc.identifier.wos000683684700023


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