Paper Info
Title
Improved Unsupervised Learning Method for Material-Properties Identification Based on Mode Separation of Ultrasonic Guided Waves
Abstract
Numerical methods, including machine-learning methods, are now actively used in applications related to elastic guided wave propagation phenomena. The method proposed in this study for material-properties characterization is based on an algorithm of the clustering of multivariate data series obtained as a result of the application of the matrix pencil method to the experimental data. In the developed technique, multi-objective optimization is employed to improve the accuracy of the identification of particular parameters. At the first stage, the computationally efficient method based on the calculation of the Fourier transform of Green's matrix is employed iteratively and the obtained solution is used for filter construction with decreasing bandwidths providing nearly noise-free classified data (with mode separation). The filter provides data separation between all guided waves in a natural way, which is needed at the second stage, where a more laborious method based on the minimization of the slowness residuals is applied to the data. The method might be further employed for material properties identification in plates with thin coatings/interlayers, multi-layered anisotropic laminates, etc.
Year
DOI
Venue
2022
10.3390/computation10060093
COMPUTATION
Keywords
DocType
Volume
laminate, material properties, identification, guided waves, mode separation
Journal
10
Issue
ISSN
Citations 
6
2079-3197
0
PageRank 
References 
Authors
0.34
4
6
Name
Order
Citations
PageRank
Mikhail V. Golub110.75
Olga V. Doroshenko210.75
Mikhail A. Arsenov310.75
Artem A. Eremin410.75
Yan Gu55710.46
Ilya A. Bareiko610.75