Original title: Deep learning methods for the acoustic emission methods to evaluate an onset of plastic straining
Authors: Parma, Slavomír ; Kovanda, Martin ; Chlada, Milan ; Štefan, Jan ; Kober, Jan ; Feigenbaum, H. P. ; Plešek, Jiří
Document type: Papers
Conference/Event: Engineering Mechanics 2023 /29./, Milovy (CZ), 20230509
Year: 2023
Language: eng
Abstract: 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\nmaterial. 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.
Keywords: acoustic emission; metal plasticity; neural networks; strain hardening
Project no.: GA23-05338S (CEP)
Funding provider: GA ČR
Host item entry: Engineering Mechanics 2023 : 29th International Conference, ISBN 978-80-87012-84-0, ISSN 1805-8248

Institution: Institute of Thermomechanics AS ČR (web)
Document availability information: Fulltext is available at external website.
External URL: https://www.engmech.cz/improc/2023/187.pdf
Original record: https://hdl.handle.net/11104/0350003

Permalink: http://www.nusl.cz/ntk/nusl-538531


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