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Statements

Subject Item
n2:RIV%2F61989100%3A27360%2F13%3A86086427%21RIV14-MSM-27360___
rdf:type
skos:Concept n11:Vysledek
dcterms:description
The subject of this paper is to design and verify the neural prediction model for predicting the occurrence of internal defects in rolled products from Cr-Mo steels. The detection of internal defects in rolled products is performed using ultrasound control of cooled rolled products; therefore this is very complicated and extensive problem. Model developed using artificial neural networks for prediction of defects in rolled products appears as an alternative to traditional methods, such as statistical regression analysis, and it is able to express more complex relations than these methods. The model predicts internal defects of rolled products on the base of the input parameters such as chemical composition and selected technological operations. The subject of this paper is to design and verify the neural prediction model for predicting the occurrence of internal defects in rolled products from Cr-Mo steels. The detection of internal defects in rolled products is performed using ultrasound control of cooled rolled products; therefore this is very complicated and extensive problem. Model developed using artificial neural networks for prediction of defects in rolled products appears as an alternative to traditional methods, such as statistical regression analysis, and it is able to express more complex relations than these methods. The model predicts internal defects of rolled products on the base of the input parameters such as chemical composition and selected technological operations.
dcterms:title
PREDICTION OF INTERNAL DEFECTS IN ROLLED PRODUCTS FROM CR-MO STEELS USING ARTIFICIAL INTELLIGENCE METHODS PREDICTION OF INTERNAL DEFECTS IN ROLLED PRODUCTS FROM CR-MO STEELS USING ARTIFICIAL INTELLIGENCE METHODS
skos:prefLabel
PREDICTION OF INTERNAL DEFECTS IN ROLLED PRODUCTS FROM CR-MO STEELS USING ARTIFICIAL INTELLIGENCE METHODS PREDICTION OF INTERNAL DEFECTS IN ROLLED PRODUCTS FROM CR-MO STEELS USING ARTIFICIAL INTELLIGENCE METHODS
skos:notation
RIV/61989100:27360/13:86086427!RIV14-MSM-27360___
n11:predkladatel
n12:orjk%3A27360
n3:aktivita
n18:S n18:P
n3:aktivity
P(ED0040/01/01), P(EE2.3.30.0016), S
n3:dodaniDat
n10:2014
n3:domaciTvurceVysledku
n4:2407558 n4:9573410 n4:8578834 n4:6375669 n4:3413357
n3:druhVysledku
n20:D
n3:duvernostUdaju
n17:S
n3:entitaPredkladatele
n13:predkladatel
n3:idSjednocenehoVysledku
98590
n3:idVysledku
RIV/61989100:27360/13:86086427
n3:jazykVysledku
n21:eng
n3:klicovaSlova
neural network, vanadium micro-alloying, internal defect, FEM
n3:klicoveSlovo
n5:vanadium%20micro-alloying n5:FEM n5:internal%20defect n5:neural%20network
n3:kontrolniKodProRIV
[AFF59BBFBD00]
n3:mistoKonaniAkce
Brno
n3:mistoVydani
Ostrava
n3:nazevZdroje
METAL 2013 : 22nd International Conference on Metallurgy and Materials : conference proceedings : May 15th - 17th 2013, Hotel Voronez I, Brno Czech Republic, EU [CD-ROM]
n3:obor
n16:JG
n3:pocetDomacichTvurcuVysledku
5
n3:pocetTvurcuVysledku
5
n3:projekt
n7:ED0040%2F01%2F01 n7:EE2.3.30.0016
n3:rokUplatneniVysledku
n10:2013
n3:tvurceVysledku
Jančíková, Zora Meca, Roman Zimný, Ondřej Kvíčala, Miroslav Koštial, Pavol
n3:typAkce
n22:WRD
n3:zahajeniAkce
2013-03-15+01:00
s:numberOfPages
6
n15:hasPublisher
Tanger s.r.o.
n19:isbn
978-80-87294-41-3
n6:organizacniJednotka
27360