About: Neural network prediction of fracture toughness from tensile test

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rdf:type	skos:Concept http://linked.opendata.cz/ontology/domain/vavai/Vysledek
Description	The reference temperature localizing the fracture toughness temperature diagram on temperature axis was predicted based on tensile test data. Regularization neural network was developed to solve the correlation between these properties. First of all standard methodology of testing was applied to determine fracture toughness from three-point bend specimens. The fracture toughness transition dependence was quantified by means of master curve concept enabling to represent it using one parameter, i.e. reference temperature. The reference temperature was calculated applying the multi-temperature method. In next the different strength and deformation characteristics and parameters were determined from standard tensile specimens focusing on data from localized deformation during specimen necking. Tensile samples with circumferential notch were also examined. In total 29 data sets from low-alloy steels were applied for the analyses. A very promising correlation of predicted and experimentally determined value The reference temperature localizing the fracture toughness temperature diagram on temperature axis was predicted based on tensile test data. Regularization neural network was developed to solve the correlation between these properties. First of all standard methodology of testing was applied to determine fracture toughness from three-point bend specimens. The fracture toughness transition dependence was quantified by means of master curve concept enabling to represent it using one parameter, i.e. reference temperature. The reference temperature was calculated applying the multi-temperature method. In next the different strength and deformation characteristics and parameters were determined from standard tensile specimens focusing on data from localized deformation during specimen necking. Tensile samples with circumferential notch were also examined. In total 29 data sets from low-alloy steels were applied for the analyses. A very promising correlation of predicted and experimentally determined value (en)
Title	Neural network prediction of fracture toughness from tensile test Neural network prediction of fracture toughness from tensile test (en)
skos:prefLabel	Neural network prediction of fracture toughness from tensile test Neural network prediction of fracture toughness from tensile test (en)
skos:notation	RIV/00216305:26210/10:PU92361!RIV11-MSM-26210___
http://linked.open...avai/riv/aktivita	S
http://linked.open...avai/riv/aktivity	S
http://linked.open...vai/riv/dodaniDat	2011
http://linked.open...aciTvurceVysledku	Hadraba, Hynek Dlouhý, Ivo Stratil, Luděk Al Khaddour, Samer
http://linked.open.../riv/druhVysledku	O - Ostatní výsledky nezařaditelné do žádného z výše uvedených druhů výsledku
http://linked.open...iv/duvernostUdaju	S - Úplné a pravdivé údaje nepodléhající ochraně podle zvláštních právních předpisů
http://linked.open...titaPredkladatele	Vysoké učení technické v Brně / Fakulta strojního inženýrství
http://linked.open...dnocenehoVysledku	274504
http://linked.open...ai/riv/idVysledku	RIV/00216305:26210/10:PU92361
http://linked.open...riv/jazykVysledku	eng - angličtina
http://linked.open.../riv/klicovaSlova	Brittle to ductile transition, Fracture Toughness, Artificial neural network, Low-alloy steels (en)
http://linked.open.../riv/klicoveSlovo	Artificial neural network Fracture Toughness Brittle to ductile transition Low-alloy steels
http://linked.open...ontrolniKodProRIV	[A2D4239903FC]
http://linked.open...in/vavai/riv/obor	JG
http://linked.open...ichTvurcuVysledku	4 (xsd:int)
http://linked.open...cetTvurcuVysledku	4 (xsd:int)
http://linked.open...UplatneniVysledku	2010
http://linked.open...iv/tvurceVysledku	Dlouhý, Ivo Hadraba, Hynek Stratil, Luděk Al Khaddour, Samer
http://localhost/t...ganizacniJednotka	26210

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