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Artificial Neural Network Modelling of Glass Laminate Sample Shape Influence on the ESPI Modes

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dc.title Artificial Neural Network Modelling of Glass Laminate Sample Shape Influence on the ESPI Modes en
dc.contributor.author Jančíková, Zora
dc.contributor.author Koštial, Pavel
dc.contributor.author Rusnáková, Soňa
dc.contributor.author Jošta, Petr
dc.contributor.author Ružiak, Ivan
dc.contributor.author David, Jiří
dc.contributor.author Valíčej, Jan
dc.contributor.author Frydrýšek, Karel
dc.relation.ispartof Mechanics and Properties of Composed Materials and Structures Advanced Structured Materials
dc.identifier.isbn 978-3-642-31497-1
dc.date.issued 2012
dc.event.location Heidelberg
utb.event.state-en Germany
utb.event.state-cs Německo
dc.type bookPart
dc.language.iso en
dc.publisher Springer-Verlag. Berlin
dc.subject artificial neural networks en
dc.subject glass laminate en
dc.subject resonance frequencies en
dc.subject electronic speckle pattern interferometry en
dc.subject finite element method en
dc.description.abstract The present work is devoted to the applications of artificial neural networks (ANN) for material design prediction. We have investigated the dependence of the generated mode frequency as a function of a sample thickness and a sample shape of glass laminate samples by electronic speckle interferometry (ESPI). The obtained experimental results for differently shaped (thickness, canting and rounding) glass laminate samples are compared with those of ANN. The coincidence of both experimental and simulated results is very good. en
utb.faculty Faculty of Technology
dc.identifier.uri http://hdl.handle.net/10563/1005934
utb.identifier.rivid RIV/70883521:28110/12:43869179!RIV13-MSM-28110___
utb.source c-riv
dc.date.accessioned 2016-04-28T10:37:25Z
dc.date.available 2016-04-28T10:37:25Z
dc.description.sponsorship P(FR-TI1/319), V
dc.format.extent 197
utb.identifier.nkp 6123588
utb.contributor.internalauthor Rusnáková, Soňa
riv.obor JI
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