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 the applications related to guided wave propagation. The method proposed in this study for material properties characterization is based on the 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 proposed technique, multi-objective optimization is employed to improve the accuracy of particular parameter identification. 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 the filter construction with decreasing bandwidth, which allows us to obtain 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 the slower method based on the minimization of the slowness residuals is applied to the data. The method might be applied for material properties identification in plates with thin coatings/interlayers, multi-layered anisotropic laminates etc.