A repeatability study of artificial neural network predictions in flow stress model development for a magnesium alloy

• Autorzy: Siewior Hubert , Madej Łukasz

Identyfikatory

DOI

10.7494/cmms.2021.4.0769

• Języki publikacji: EN

Abstrakty

EN

This work is devoted to an evaluation of the capabilities of artificial neural networks (ANN) in terms of developing a flow stress model for magnesium ZE20. The learning procedure is based on experimental flow-stress data following inverse analysis. Two types of artificial neural networks are investigated: a simple feedforward version and a recursive one. Issues related to the quality of input data and the size of the training dataset are presented and discussed. The work confirms the general ability of feedforward neural networks in flow stress data predictions. It also highlights that slightly better quality predictions are obtained using recursive neural networks.

Słowa kluczowe

EN

flow stress; artificial neural networks; feedforward; recursive

• Wydawca: Wydawnictwa AGH
• Czasopismo: Computer Methods in Materials Science
• Rocznik: 2021
• Tom: Vol. 21, No. 4
• Strony: 209--218
• Opis fizyczny: Bibliogr. 20 poz., rys.

Twórcy

autor

Siewior Hubert

• AGH University of Science and Technology, al. A. Mickiewicza 30, 30-059, Krakow, Poland

autor

Madej Łukasz

• AGH University of Science and Technology, al. A. Mickiewicza 30, 30-059, Krakow, Poland

• https://orcid.org/0000-0003-1032-6963

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).