Peak Point Description Utilizing of an Artificial Neural Network Approach in Comparison with the Commonly Used Relationships

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27360%2F18%3A10241028" target="_blank" >RIV/61989100:27360/18:10241028 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Peak Point Description Utilizing of an Artificial Neural Network Approach in Comparison with the Commonly Used Relationships

Popis výsledku v původním jazyce

The peak point coordinates (i.e. peak stress, peak strain) play a significant role in case of a flow curve description. These coordinates are strongly dependent on the temperature and strain rate, so they need to be related to these thermomechanical circumstances before use in the flow stress models. In this research, the experimental peak point coordinates of the C45 and 38MnVS6 steels were described in a wide range of thermomechanical conditions by use of two different methodologies. The first one was based on the ordinary predictive relationships utilizing the well-known Zener-Hollomon parameter. The second one was based on the artificial neural network approach. The aim was to compare appropriateness of these methods. The results have suggested better aptness in case of the assembled neural networks.

Název v anglickém jazyce

Peak Point Description Utilizing of an Artificial Neural Network Approach in Comparison with the Commonly Used Relationships

Popis výsledku anglicky

The peak point coordinates (i.e. peak stress, peak strain) play a significant role in case of a flow curve description. These coordinates are strongly dependent on the temperature and strain rate, so they need to be related to these thermomechanical circumstances before use in the flow stress models. In this research, the experimental peak point coordinates of the C45 and 38MnVS6 steels were described in a wide range of thermomechanical conditions by use of two different methodologies. The first one was based on the ordinary predictive relationships utilizing the well-known Zener-Hollomon parameter. The second one was based on the artificial neural network approach. The aim was to compare appropriateness of these methods. The results have suggested better aptness in case of the assembled neural networks.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

20501 - Materials engineering

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)

Rok uplatnění

2018

Kód důvěrnosti údajů

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

Název statě ve sborníku

METAL 2018 : 27th International Conference on Metallurgy and Materials : abstracts : May 23rd-25th 2018, Hotel Voronez I, Brno, Czech Republic, EU

ISBN

978-80-87294-83-3

ISSN

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e-ISSN

neuvedeno

Počet stran výsledku

6

Strana od-do

432-437

Název nakladatele

Tanger

Místo vydání

Ostrava

Místo konání akce

Brno

Datum konání akce

23. 5. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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