Název: Deep learning methods for acoustic emission evaluation
Autoři: Kovanda, M. ; Chlada, Milan
Typ dokumentu: Příspěvky z konference
Konference/Akce: SPMS 2020/21, Malá Skála (CZ), 20210624
Rok: 2021
Jazyk: eng
Abstrakt: 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.
Klíčová slova: acoustic emission; deep learning; machine learning; plastic deformation; time series classification
Zdrojový dokument: SPMS 2020/21 Stochastic and Physical Monitoring Systems, ISBN 978-80-01-06922-6

Instituce: Ústav termomechaniky AV ČR (web)
Informace o dostupnosti dokumentu: Dokument je dostupný v příslušném ústavu Akademie věd ČR.
Původní záznam: http://hdl.handle.net/11104/0327457

Trvalý odkaz NUŠL: http://www.nusl.cz/ntk/nusl-508297


Záznam je zařazen do těchto sbírek:
Věda a výzkum > AV ČR > Ústav termomechaniky
Konferenční materiály > Příspěvky z konference