Szczegóły publikacji
Opis bibliograficzny
A repeatability study of artificial neural network predictions in flow stress model development for a magnesium alloy / Hubert Siewior, Łukasz MADEJ // Computer Methods in Materials Science : quarterly / Akademia Górniczo-Hutnicza ; ISSN 2720-4081. — Tytuł poprz.: Informatyka w Technologii Materiałów ; ISSN: 1641-3948. — 2021 — vol. 21 no. 4, s. 209–217. — Bibliogr. s. 216–217, Abstr.
Autorzy (2)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 145762 |
|---|---|
| Data dodania do BaDAP | 2023-03-23 |
| Tekst źródłowy | URL |
| DOI | 10.7494/cmms.2021.4.0769 |
| Rok publikacji | 2021 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Computer Methods in Materials Science |
Abstract
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.
