Sequential simulation and neural network in the stress–strain curve identification over the large strains using tensile test

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26210%2F17%3APU123821" target="_blank" >RIV/00216305:26210/17:PU123821 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/s00419-017-1234-0" target="_blank" >http://dx.doi.org/10.1007/s00419-017-1234-0</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s00419-017-1234-0" target="_blank" >10.1007/s00419-017-1234-0</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Sequential simulation and neural network in the stress–strain curve identification over the large strains using tensile test

Popis výsledku v původním jazyce

Two alternative methods for the stress–strain curve determination in the large strains region are proposed. Only standard force–elongation response is needed as an input into the identification procedure. Both methods are applied to eight various materials, covering a broad spectre of possible ductile behaviour. The first method is based on the iterative procedure of sequential simulation of piecewise stress–strain curve using the parallel finite element modelling. Error between the computed and experimental force–elongation response is low, while the convergence rate is high. The second method uses the neural network for the stress–strain curve identification. Large database of force–elongation responses is computed by the finite element method. Then, the database is processed and reduced in order to get the input for neural network training procedure. Training process and response of network is fast compared to sequential simulation. When the desired accuracy is not reached, results can be used as a starting point for the following optimization task.

Název v anglickém jazyce

Sequential simulation and neural network in the stress–strain curve identification over the large strains using tensile test

Popis výsledku anglicky

Two alternative methods for the stress–strain curve determination in the large strains region are proposed. Only standard force–elongation response is needed as an input into the identification procedure. Both methods are applied to eight various materials, covering a broad spectre of possible ductile behaviour. The first method is based on the iterative procedure of sequential simulation of piecewise stress–strain curve using the parallel finite element modelling. Error between the computed and experimental force–elongation response is low, while the convergence rate is high. The second method uses the neural network for the stress–strain curve identification. Large database of force–elongation responses is computed by the finite element method. Then, the database is processed and reduced in order to get the input for neural network training procedure. Training process and response of network is fast compared to sequential simulation. When the desired accuracy is not reached, results can be used as a starting point for the following optimization task.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20302 - Applied mechanics

Projekt

<a href="/cs/project/LO1202" target="_blank" >LO1202: NETME CENTRE PLUS</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2017

Kód důvěrnosti údajů

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

Název periodika

ARCHIVE OF APPLIED MECHANICS

ISSN

0939-1533

e-ISSN

1432-0681

Svazek periodika

87

Číslo periodika v rámci svazku

6

Stát vydavatele periodika

DE - Spolková republika Německo

Počet stran výsledku

17

Strana od-do

1077-1093

Kód UT WoS článku

000403361400010

EID výsledku v databázi Scopus

2-s2.0-85014578067