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

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    0573751 - ÚT 2024 CZ eng C - Conference Paper (international conference)
    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]
    R&D Projects: GA ČR GA23-05338S
    Institutional support: RVO:61388998
    Keywords : metal plasticity * strain hardening * acoustic emission * neural networks
    OECD category: 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.
    Permanent Link: https://hdl.handle.net/11104/0350003

     
     
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