Neural network prediction of fracture toughness from tensile test

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26210%2F10%3APU92361" target="_blank" >RIV/00216305:26210/10:PU92361 - isvavai.cz</a>

Výsledek na webu

—

DOI - Digital Object Identifier

—

Jazyk výsledku

angličtina

Název v původním jazyce

Neural network prediction of fracture toughness from tensile test

Popis výsledku v původním jazyce

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

Název v anglickém jazyce

Neural network prediction of fracture toughness from tensile test

Popis výsledku anglicky

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

Druh

O - Ostatní výsledky

CEP obor

JG - Hutnictví, kovové materiály

OECD FORD obor

—

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2010

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů