Zobrazit minimální záznam

dc.contributor.authorSeidl, David
dc.contributor.authorRužiak, Ivan
dc.contributor.authorKoštialová Jančíková, Zora
dc.contributor.authorKoštial, Pavol
dc.date.accessioned2022-10-12T06:58:43Z
dc.date.available2022-10-12T06:58:43Z
dc.date.issued2022
dc.identifier.citationExpert Systems with Applications. 2022, vol. 208, art. no. 118039.cs
dc.identifier.issn0957-4174
dc.identifier.issn1873-6793
dc.identifier.urihttp://hdl.handle.net/10084/148726
dc.description.abstractIn this article, we examine the use of sensitivity analysis for the optimization of selected physical properties in rubber compounds and determine objective criteria which allow for the reduction of environmental load during rubber compound production. The sensitivity analysis shows how significantly each input value affects the output value, and the response graphs express the effect of the selected parameter on the output value. The solutions described in the article are applicable to other production technologies. We present a sensitivity analysis based on the prediction of selected mechanical properties of rubber mixtures composed of Standard Malaysian Rubber (SMR). Two blends were pre- pared by mixing SMR and oleic acid and different concentrations of surfactant (2, 4, 6, 8, 10, 20, 30 wt%). Tensile strength Rm and moduli M100, M200, M300 were measured and evaluated. The sensitivity analysis showed the significance of certain ingredients which affect the measured mechanical properties.cs
dc.language.isoencs
dc.publisherElseviercs
dc.relation.ispartofseriesExpert Systems with Applicationscs
dc.relation.urihttps://doi.org/10.1016/j.eswa.2022.118039cs
dc.rights© 2022 The Author(s). Published by Elsevier Ltd.cs
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/cs
dc.subjectrubber blendscs
dc.subjectplasticizerscs
dc.subjectartificial neural networkscs
dc.subjectsensitivity analysiscs
dc.subjectpredictionscs
dc.titleSensitivity analysis: A tool for tailoring environmentally friendly materialscs
dc.typearticlecs
dc.identifier.doi10.1016/j.eswa.2022.118039
dc.rights.accessopenAccesscs
dc.type.versionpublishedVersioncs
dc.type.statusPeer-reviewedcs
dc.description.sourceWeb of Sciencecs
dc.description.volume208cs
dc.description.firstpageart. no. 118039cs
dc.identifier.wos000835492600007


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Zobrazit minimální záznam

© 2022 The Author(s). Published by Elsevier Ltd.
Kromě případů, kde je uvedeno jinak, licence tohoto záznamu je © 2022 The Author(s). Published by Elsevier Ltd.