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Statements

Subject Item
n2:RIV%2F68407700%3A21220%2F14%3A00224869%21RIV15-MSM-21220___
rdf:type
n7:Vysledek skos:Concept
rdfs:seeAlso
http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6889799
dcterms:description
In order to improve corrosion resistance of alloy S355 EN 1025, the relationship between the thickness of zinc coating created during the process of acidic galvanic zincing and factors that influence this process were investigated. Influence of individual factors on thickness of zinc coating for sample area with surface current density of 3 A.dm2 was determined by planned experiment which uses central composite plan. The obtained experimental data were evaluated based on neural network theory using cubic neural unit with Levenberg-Marquardt iterative adaptive algorithm. The influence of number of training data on the reliability of the obtained computational model has been studied. Furthermore, relationship between the amount of training data and reliability of prediction for the thickness of created zinc layer was observed. The relationship between input factors and thickness of layer coating with 88.37% reliability was reached. In order to improve corrosion resistance of alloy S355 EN 1025, the relationship between the thickness of zinc coating created during the process of acidic galvanic zincing and factors that influence this process were investigated. Influence of individual factors on thickness of zinc coating for sample area with surface current density of 3 A.dm2 was determined by planned experiment which uses central composite plan. The obtained experimental data were evaluated based on neural network theory using cubic neural unit with Levenberg-Marquardt iterative adaptive algorithm. The influence of number of training data on the reliability of the obtained computational model has been studied. Furthermore, relationship between the amount of training data and reliability of prediction for the thickness of created zinc layer was observed. The relationship between input factors and thickness of layer coating with 88.37% reliability was reached.
dcterms:title
Application of neural networks to evaluate experimental data of galvanic zincing Application of neural networks to evaluate experimental data of galvanic zincing
skos:prefLabel
Application of neural networks to evaluate experimental data of galvanic zincing Application of neural networks to evaluate experimental data of galvanic zincing
skos:notation
RIV/68407700:21220/14:00224869!RIV15-MSM-21220___
n4:aktivita
n12:S
n4:aktivity
S
n4:dodaniDat
n11:2015
n4:domaciTvurceVysledku
n10:8200599
n4:druhVysledku
n15:D
n4:duvernostUdaju
n22:S
n4:entitaPredkladatele
n17:predkladatel
n4:idSjednocenehoVysledku
3791
n4:idVysledku
RIV/68407700:21220/14:00224869
n4:jazykVysledku
n13:eng
n4:klicovaSlova
neural networks; galvanic zincing; Levenberg-Marquard; cubic neural unit
n4:klicoveSlovo
n9:Levenberg-Marquard n9:cubic%20neural%20unit n9:neural%20networks n9:galvanic%20zincing
n4:kontrolniKodProRIV
[C1C6D378A183]
n4:mistoKonaniAkce
Beijing
n4:mistoVydani
Piscataway
n4:nazevZdroje
Neural Networks (IJCNN), 2014 International Joint Conference on - Scopus ISBN
n4:obor
n20:JK
n4:pocetDomacichTvurcuVysledku
1
n4:pocetTvurcuVysledku
4
n4:rokUplatneniVysledku
n11:2014
n4:tvurceVysledku
Pitel, J. Michal, Peter Bukovský, Ivo Vagaska, A.
n4:typAkce
n16:WRD
n4:zahajeniAkce
2014-07-06+02:00
s:numberOfPages
5
n18:doi
10.1109/IJCNN.2014.6889799
n5:hasPublisher
IEEE
n19:isbn
978-1-4799-1484-5
n14:organizacniJednotka
21220