Methods of artificial intelligence for acoustic emission diagnostics of fracture stages (A review). P. 1: Algorithms of unsupervised and supervised machine learning

Keywords

acoustic emission, machine unsupervised and supervised learning, identification of defects.

Cite as

Stankevych O. M. and Rebot D. P. Methods of artificial intelligence for acoustic emission diagnostics of fracture stages (A review). P. 1: Algorithms of unsupervised and supervised machine learning. Physicochemical Mechanics of Materials. 2024. 60(3), 005-014.

https://doi.org/10.15407/pcmm2024.03.005

Abstract

Based on the analysis of the latest studies, the possibilities of using unsupervised and supervised machine learning algorithms for automating the processing of acoustic emis-sion signals to identify and localize their sources are considered. The accuracy of the results for different approaches is compared and directions for its improvement are described. The importance of further research regarding the adaptation and optimization of the latest techniques for various materials and structures is confirmed.

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