Original title: Deep learning methods for acoustic emission evaluation
Authors: Kovanda, M. ; Chlada, Milan
Document type: Papers
Conference/Event: SPMS 2020/21, Malá Skála (CZ), 20210624
Year: 2021
Language: eng
Abstract: The goal of this paper is to show the possibilities of state-of-the-art deep learning methods for ultrasound signals evaluation. Several neural network architectures are applied to\nacoustic emission signals measured during the tensile tests of metallic specimen to determine the beginning of plasticity in the material. Plastic deformation is accompanied by microscopic\nevents such as a slip of atomic plane dislocations which is hardly detectable by other methods. The potential of machine learning is demonstrated on two tensile tests where the material is\nstrained until it collapses. The examined networks proved well to reliably predict the risk of collapse together with changes in the ultrasound emission signals.
Keywords: acoustic emission; deep learning; machine learning; plastic deformation; time series classification
Host item entry: SPMS 2020/21 Stochastic and Physical Monitoring Systems, ISBN 978-80-01-06922-6

Institution: Institute of Thermomechanics AS ČR (web)
Document availability information: Fulltext is available at the institute of the Academy of Sciences.
Original record: http://hdl.handle.net/11104/0327457

Permalink: http://www.nusl.cz/ntk/nusl-508297


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Research > Institutes ASCR > Institute of Thermomechanics
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