Possibilities of Assembling of Processing Maps by Utilizing of an Artificial Neural Network Approach
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27360%2F18%3A10241040" target="_blank" >RIV/61989100:27360/18:10241040 - isvavai.cz</a>
Výsledek na webu
<a href="https://iopscience.iop.org/article/10.1088/1757-899X/461/1/012063/meta" target="_blank" >https://iopscience.iop.org/article/10.1088/1757-899X/461/1/012063/meta</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1088/1757-899X/461/1/012063" target="_blank" >10.1088/1757-899X/461/1/012063</a>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Possibilities of Assembling of Processing Maps by Utilizing of an Artificial Neural Network Approach
Popis výsledku v původním jazyce
The processing maps (i.e. power dissipation maps superimposed over instability maps) can be used as a very convenient tool in case of an optimizing of hot forming processes. In this research, processing maps of C45 medium-carbon steel were assembled on the basis of an experimental flow stress dataset. This dataset was acquired via series of uniaxial hot compression tests in the temperature range of 1173 K - 1553 K and the strain rate range of 0.1 1/s - 100 1/s. In addition, a predicted flow stress dataset was created with use of an artificial neural network approach - it allowed extending of the experimental dataset with additional temperature levels. The experimentally compiled processing maps have been subsequently enhanced by this additional dataset to encourage the overall information capability. The results have showed that the predicted dataset was useful to reveal additional instability regions in the experimentally assembled processing maps.
Název v anglickém jazyce
Possibilities of Assembling of Processing Maps by Utilizing of an Artificial Neural Network Approach
Popis výsledku anglicky
The processing maps (i.e. power dissipation maps superimposed over instability maps) can be used as a very convenient tool in case of an optimizing of hot forming processes. In this research, processing maps of C45 medium-carbon steel were assembled on the basis of an experimental flow stress dataset. This dataset was acquired via series of uniaxial hot compression tests in the temperature range of 1173 K - 1553 K and the strain rate range of 0.1 1/s - 100 1/s. In addition, a predicted flow stress dataset was created with use of an artificial neural network approach - it allowed extending of the experimental dataset with additional temperature levels. The experimentally compiled processing maps have been subsequently enhanced by this additional dataset to encourage the overall information capability. The results have showed that the predicted dataset was useful to reveal additional instability regions in the experimentally assembled processing maps.
Klasifikace
Druh
D - Stať ve sborníku
CEP obor
—
OECD FORD obor
20501 - Materials engineering
Návaznosti výsledku
Projekt
<a href="/cs/project/LO1203" target="_blank" >LO1203: Regionální materiálově technologické výzkumné centrum - Program udržitelnosti</a><br>
Návaznosti
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Ostatní
Rok uplatnění
2018
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název statě ve sborníku
IOP Conference Series: Materials Science and Engineering. Volume 461
ISBN
—
ISSN
1757-8981
e-ISSN
1757-899X
Počet stran výsledku
6
Strana od-do
"Neuveden"
Název nakladatele
IOP Publishing
Místo vydání
Bristol
Místo konání akce
Plzeň
Datum konání akce
14. 11. 2018
Typ akce podle státní příslušnosti
WRD - Celosvětová akce
Kód UT WoS článku
—