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dc.contributor.authorOpěla, Petr
dc.contributor.authorSchindler, Ivo
dc.contributor.authorKawulok, Petr
dc.contributor.authorKawulok, Rostislav
dc.contributor.authorRusz, Stanislav
dc.contributor.authorNavrátil, Horymír
dc.contributor.authorJurča, Radek
dc.date.accessioned2020-10-23T11:04:59Z
dc.date.available2020-10-23T11:04:59Z
dc.date.issued2020
dc.identifier.citationMaterials. 2020, vol. 13, issue 16, art. no. 3480.cs
dc.identifier.issn1996-1944
dc.identifier.urihttp://hdl.handle.net/10084/142352
dc.description.abstractIn the presented research, conventional hot processing maps superimposed over the flow stress maps or activation energy maps are utilized to study a correlation among the efficiency of power dissipation, flow stress, and activation energy evolution in the case of Cr-Mo low-alloyed steel. All maps have been assembled on the basis of two flow curve datasets. The experimental one is the result of series of uniaxial hot compression tests. The predicted one has been calculated on the basis of the subsequent approximation procedure via a well-adapted artificial neural network. It was found that both flow stress and activation energy evolution are capable of expressing changes in the studied steel caused by the hot compression deformation. A direct association with the course of power dissipation efficiency is then evident in the case of both. The connection of the presence of instability districts to the activation energy evolution, flow stress course, and power dissipation efficiency was discussed further. Based on the obtained findings it can be stated that the activation energy processing maps represent another tool for the finding of appropriate forming conditions and can be utilized as a support feature for the conventionally-used processing maps to extend their informative ability.cs
dc.language.isoencs
dc.publisherMDPIcs
dc.relation.ispartofseriesMaterialscs
dc.relation.urihttp://doi.org/10.3390/ma13163480cs
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.subjectprocessing mapscs
dc.subjectactivation energy mapscs
dc.subjectflow stress mapscs
dc.subjectartificial neural networkscs
dc.titleCorrelation among the power dissipation efficiency, flow stress course, and activation energy evolution in Cr-Mo low-alloyed steelcs
dc.typearticlecs
dc.identifier.doi10.3390/ma13163480
dc.rights.accessopenAccesscs
dc.type.versionpublishedVersioncs
dc.type.statusPeer-reviewedcs
dc.description.sourceWeb of Sciencecs
dc.description.volume13cs
dc.description.issue16cs
dc.description.firstpageart. no. 3480cs
dc.identifier.wos000567687100001


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© 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.
Except where otherwise noted, this item's license is described as © 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.