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IDENTIFICATION OF ACOUSTIC EMISSION SOURCES IN A POLIMER COMPOSITE MATERIAL UNDER THE CYCLE TENSION LOADING
Author(s) -
A. A. Bryansky,
О. В. Башков
Publication year - 2021
Publication title -
vektor nauki tolʹâttinskogo gosudarstvennogo universiteta
Language(s) - English
Resource type - Journals
eISSN - 2712-8458
pISSN - 2073-5073
DOI - 10.18323/2073-5073-2021-3-19-27
Subject(s) - acoustic emission , materials science , composite material , tension (geology) , glass fiber , cluster analysis , fracture (geology) , stiffness , composite number , structural engineering , ultimate tensile strength , computer science , engineering , machine learning
The structure of polymer composite materials (PCM) provides high mechanical properties but, at the same time, is highly sensitive to the formation of internal defects. Therefore, when designing, manufacturing products, and assessing their reliability in service, much attention is paid to the methods of non-destructive testing, among which the method of acoustic emission (AE) has proven itself to study structural changes in material under external influence. The paper deals with the identification of typical damages in fiberglass samples made of T11-GVS9 glass fiber cloth and DION 9300 FR binder and tested under cyclic tension using the AE method. In the work, the authors solved the problem of selecting the AE informative parameters and used a clustering method to identify the nature and the formation kinetics of the AE sources. The authors performed clustering using the Kohonen self-organization map (SOM) with the Fourier spectra calculated for the AE signals recorded during cyclic tests. Based on the peak frequencies analysis of the produced clusters, the researchers determined their nature and calculated the periods of critical accumulation. When characterizing the AE sources, the authors used the peak frequencies analysis of the wavelet spectra performed for different levels of decomposition. The authors determined the damage accumulation stages of samples during testing based on own research and research by other authors’ results. The study established that registration of AE signals identified as adhesion failure can be used to identify the onset of the material destruction and characterized the local formation of micro-damages in the matrix and fracture of fibers can be used to predict the destruction of PCM.

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