Deep learning methods for acoustic emission evaluation

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61388998%3A_____%2F21%3A00549679" target="_blank" >RIV/61388998:_____/21:00549679 - isvavai.cz</a>
Alternative codes found
RIV/68407700:21340/21:00353114
Result on the web
—
DOI - Digital Object Identifier
—

Result language
angličtina
Original language name
Deep learning methods for acoustic emission evaluation
Original language description
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 tonacoustic emission signals measured during the tensile tests of metallic specimen to determine the beginning of plasticity in the material. Plastic deformation is accompanied by microscopicnevents 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 isnstrained until it collapses. The examined networks proved well to reliably predict the risk of collapse together with changes in the ultrasound emission signals.
Czech name
—
Czech description
—

Project
—
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
SPMS 2020/21 Stochastic and Physical Monitoring Systems
ISBN
978-80-01-06922-6
ISSN
—
e-ISSN
—
Number of pages
8
Pages from-to
111-118
Publisher name
Czech Technical University in Prague
Place of publication
Praha
Event location
Malá Skála
Event date
Jun 24, 2021
Type of event by nationality
EUR - Evropská akce
UT code for WoS article
—

Similar results(10)