Deep learning methods for the acoustic emission methods to evaluate an onset of plastic straining

0573751 - ÚT 2024 CZ eng C - Konferenční příspěvek (zahraniční konf.)
Parma, Slavomír - Kovanda, Martin - Chlada, Milan - Štefan, Jan - Kober, Jan - Feigenbaum, H. P. - Plešek, Jiří
Deep learning methods for the acoustic emission methods to evaluate an onset of plastic straining.
Engineering Mechanics 2023 : 29th International Conference. Vol. 29. Prague: Institute of Thermomechanics of the Czech Academy of Sciences, 2023 - (Radolf, V.; Zolotarev, I.), s. 187-190. ISBN 978-80-87012-84-0. ISSN 1805-8248. E-ISSN 1805-8256.
[Engineering Mechanics 2023 /29./. Milovy (CZ), 09.05.2023-11.05.2023]
Grant CEP: GA ČR GA23-05338S
Institucionální podpora: RVO:61388998
Klíčová slova: metal plasticity * strain hardening * acoustic emission * neural networks
Obor OECD: Applied mechanics
https://www.engmech.cz/improc/2023/187.pdf

Development of phenomenological plasticity models, hardening rules, and plasticity theories relies on experimental data of plastic straining. The experimental data are usually measured as the stress–strain response of the material being loaded and do not provide any clues or information about the local response of
material. In this paper, we analyze the plastic deformation of the material using the acoustic emission method and current state-of-the-art neural network models such as the InceptionTime architecture.
Trvalý link: https://hdl.handle.net/11104/0350003