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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Veröffentlicht in:	Materials science (New York, N.Y.) Jg. 60; H. 3; S. 255 - 264
Hauptverfasser:	Stankevych, O. M., Rebot, D. P.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.11.2024
Schlagworte:	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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Abstract	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.
AbstractList	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.
Author	Rebot, D. P. Stankevych, O. M.
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Cites_doi	10.1016/j.apacoust.2022.108863 10.1016/j.engstruct.2023.117228 10.1016/j.compositesb.2016.09.101 10.1016/j.engfracmech.2023.109680 10.1016/j.aej.2016.12.010 10.1016/j.ultras.2023.106998 10.1016/j.jmapro.2024.01.036 10.1016/j.compositesb.2023.110774 10.1007/978-3-031-11291-1_1 10.1016/j.engfracmech.2022.108993 10.1016/j.measurement.2018.04.076 10.1016/j.ultras.2024.107249 10.1016/j.apacoust.2021.108425 10.1038/s41524-021-00565-x 10.1016/j.tafmec.2024.104300 10.1016/j.istruc.2023.105185 10.3390/s22114055 10.1007/s11003-018-0168-1 10.1007/978-981-13-7403-6_9 10.1016/j.conbuildmat.2022.129789 10.1016/j.conbuildmat.2023.133474 10.1016/j.jeurceramsoc.2020.06.070 10.1016/j.eswa.2023.119738 10.1177/26349833241244403 10.1007/s11003-024-00844-0 10.1016/j.measurement.2023.113042 10.1016/j.compstruct.2018.07.047 10.1016/j.compstruct.2020.111906 10.1007/s11003-024-00760-3 10.1007/s11003-024-00831-5 10.1016/j.addlet.2023.100130 10.1007/978-3-031-11291-1_2 10.1016/j.engfailanal.2020.104800 10.1016/j.infrared.2020.103581 10.1109/ACCESS.2021.3096930 10.1016/j.conbuildmat.2021.123541 10.1007/s10845-014-0941-4 10.1016/j.ijpvp.2020.104243 10.1007/s11223-015-9691-6 10.3390/app122010476 10.1016/j.ymssp.2013.03.017 10.30564/ssid.v2i2.1931
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SubjectTerms	Characterization and Evaluation of Materials Chemistry and Materials Science Materials Science Solid Mechanics Structural Materials
Subtitle	Part 1: algorithms of unsupervised and supervised machine learning
Title	Methods of artificial intelligence for acoustic emission diagnostics of fracture stages (a review)
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