Application of Artificial Neural Networks in Chosen Glass Laminates Properties Prediction

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F13%3A86089226" target="_blank" >RIV/61989100:27240/13:86089226 - isvavai.cz</a>
Alternative codes found
RIV/61989100:27360/13:86089226
Result on the web
<a href="http://dx.doi.org/10.1007/978-1-4614-3558-7_95" target="_blank" >http://dx.doi.org/10.1007/978-1-4614-3558-7_95</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-1-4614-3558-7_95" target="_blank" >10.1007/978-1-4614-3558-7_95</a>

Result language
angličtina
Original language name
Application of Artificial Neural Networks in Chosen Glass Laminates Properties Prediction
Original language description
The article deals with applications of the artificial neural networks at the evaluation of chosen material's properties (sample thickness, sample shape) measured by electronic speckle pattern interferometry. We have investigated the dependence of the generated mode frequency as a function of sample thickness as well as the sample shape of glass laminate samples. Obtained experimental results for differently shaped glass laminate samples are compared with those of artificial neural networks and finite element method simulation. The coincidence of both experimental and simulated results is very good. Copyright 2013 Springer Science+Business Media.
Czech name
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Czech description
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Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
JI - Composite materials
OECD FORD branch
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Publication year
2013
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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