Methods of artificial intelligence for acoustic emission diagnostics of fracture stages (a review) Part 1: algorithms of unsupervised and supervised machine learning

Based on the analysis of the latest studies, the possibilities of using unsupervised and supervised machine learning algorithms to automate the processing of acoustic emission signals to identify and localize their sources were considered. The accuracy of the results for different approaches was com...

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Vydáno v:	Materials science (New York, N.Y.) Ročník 60; číslo 3; s. 255 - 264
Hlavní autoři:	Stankevych, O. M., Rebot, D. P.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York Springer US 01.11.2024
Témata:	Characterization and Evaluation of Materials Chemistry and Materials Science Materials Science Solid Mechanics Structural Materials Acoustic emission Identification of defects Machine unsupervised and supervised learning
ISSN:	1068-820X, 1573-885X
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Popis
Shrnutí:	Based on the analysis of the latest studies, the possibilities of using unsupervised and supervised machine learning algorithms to automate the processing of acoustic emission signals to identify and localize their sources were considered. The accuracy of the results for different approaches was compared, and directions for improvement were described. The importance of further research regarding the adaptation and optimization of the latest techniques for various materials and structures was confirmed.
ISSN:	1068-820X 1573-885X
DOI:	10.1007/s11003-025-00880-4