Zobrazit minimální záznam

dc.contributor.authorOpěla, Petr
dc.contributor.authorKawulok, Petr
dc.contributor.authorSchindler, Ivo
dc.contributor.authorKawulok, Rostislav
dc.contributor.authorRusz, Stanislav
dc.contributor.authorNavrátil, Horymír
dc.date.accessioned2021-01-27T11:46:11Z
dc.date.available2021-01-27T11:46:11Z
dc.date.issued2020
dc.identifier.citationMetals. 2020, vol. 10, issue 11, art. no. 1413.cs
dc.identifier.issn2075-4701
dc.identifier.urihttp://hdl.handle.net/10084/142589
dc.description.abstractDescription of flow stress evolution, specifically an approximation of a set of flow curves acquired under a wide range of thermomechanical conditions, of various materials is often solved via so-called flow stress models. Some of these models are associated with a description of significant flow-curve coordinates. It is clear, the more accurate the coordinates description, the more accurate the assembled model. In the presented research, Zener-Hollomon-based relations, multi-layer perceptron networks and multivariate polynomials are employed to describe the peak and steady-state coordinates of an Invar 36 flow curve dataset. Comparison of the utilized methods in the case of the studied alloy has showed that the suitable description is given by the multivariate polynomials although the Zener-Hollomon and perceptron networks also offer valuable results.cs
dc.language.isoencs
dc.publisherMDPIcs
dc.relation.ispartofseriesMetalscs
dc.relation.urihttp://doi.org/10.3390/met10111413cs
dc.rights© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.cs
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/cs
dc.subjectflow stress descriptioncs
dc.subjectpeak and steady-state descriptioncs
dc.subjectregression analysiscs
dc.subjectZener-Hollomon parametercs
dc.subjectartificial neural networkscs
dc.subjectmultivariate polynomialscs
dc.titleOn the Zener-Hollomon parameter, multi-layer perceptron and multivariate polynomials in the struggle for the peak and steady-state descriptioncs
dc.typearticlecs
dc.identifier.doi10.3390/met10111413
dc.rights.accessopenAccesscs
dc.type.versionpublishedVersioncs
dc.type.statusPeer-reviewedcs
dc.description.sourceWeb of Sciencecs
dc.description.volume10cs
dc.description.issue11cs
dc.description.firstpageart. no. 1413cs
dc.identifier.wos000593233000001


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Zobrazit minimální záznam

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.
Kromě případů, kde je uvedeno jinak, licence tohoto záznamu je © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.