Artifical neural network usage for determining solidus temperature of steels

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27360%2F19%3A10243580" target="_blank" >RIV/61989100:27360/19:10243580 - 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

Artifical neural network usage for determining solidus temperature of steels

Popis výsledku v původním jazyce

The potential use of artificial neural networks to determine the solidus temperature for steel based on composition has been investigated. Input data consist of solidus composition and temperatures both from literature and both from measurements. The primary performance testing of the model was then performed for steel grades measured. Several types of network topologies have been designed and trained and the best model selected. Testing was done on previously unseen data measured by differential thermal analysis method as on new input data. The used method is described. Obtained results are then compared to those measured and calculated by commonly used software among the academic and commercial community like IDS and Thermo-Calc. Performance of these three modelling approaches is discussed by means of selected statistic tools.

Název v anglickém jazyce

Artifical neural network usage for determining solidus temperature of steels

Popis výsledku anglicky

The potential use of artificial neural networks to determine the solidus temperature for steel based on composition has been investigated. Input data consist of solidus composition and temperatures both from literature and both from measurements. The primary performance testing of the model was then performed for steel grades measured. Several types of network topologies have been designed and trained and the best model selected. Testing was done on previously unseen data measured by differential thermal analysis method as on new input data. The used method is described. Obtained results are then compared to those measured and calculated by commonly used software among the academic and commercial community like IDS and Thermo-Calc. Performance of these three modelling approaches is discussed by means of selected statistic tools.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

20500 - Materials engineering

Projekt

<a href="/cs/project/GA17-18668S" target="_blank" >GA17-18668S: Komplexní experimentální výzkum vybraných vlastností kvaternárních a quinárních systémů Fe-C-O-X(Y), (X,Y=Cr,Ni) za vysokých teplot</a><br>

Návaznosti

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

Rok uplatnění

2019

Kód důvěrnosti údajů

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

Název statě ve sborníku

METAL 2019 : conference proceedings : peer reviewed : 28th International Conference on Metallurgy and Materials : May 22nd-24th 2019, Hotel Voronez I, Brno, Czech Republic, EU

ISBN

978-80-87294-92-5

ISSN

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e-ISSN

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Počet stran výsledku

5

Strana od-do

48-53

Název nakladatele

Tanger

Místo vydání

Ostrava

Místo konání akce

Brno

Datum konání akce

22. 5. 2019

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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