Identification of elastic properties utilizing non-destructive vibrational evaluation methods with emphasis on definition of objective functions: a review

Tam, Jun Hui

doi:10.1007/s00158-019-02433-1

Cited by 16 publications

(3 citation statements)

References 94 publications

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“…Next, we would like to mention the identification and characterization of elastic properties. Tam [11] reviewed the identification of elastic properties utilizing nondestructive vibrational evaluation methods. He mentioned that the following gaps are worthy of future study: Simplex, Newton, BFGS, Gauss-Newton and SQP.…”

Section: Introductionmentioning

confidence: 99%

Objective Function Distortion Reduction in Identification Technique of Composite Material Elastic Properties

Ragauskas,

Jasevičius

2024

Vibration

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In studies of structural mechanics, modal analysis, presented in this paper, is an important tool for analyzing the vibration of an object and its frequencies. In modal analysis, different modes of vibration and the frequencies that generate them are considered. The study covers the nondestructive identification of the elastic characteristics of materials, which involves stochastic algorithms and the application of reverse engineering (i.e., the comparison of reference eigenfrequencies with the results of mathematical models). Identification is achieved by minimizing the objective function—the smaller the value of the objective function, the higher the identification accuracy obtained. By changing the parameters of a material’s mathematical model during identification, certain (usually higher order) modes can change places in a natural frequency spectrum. This leads to the comparison of different order eigenfrequencies, slow convergence and poor accuracy of the identification process. The technique involved in this work is the mode-shape recognition of a specimen of material with an “incorrect” set of elastic properties. The results prove that the identification accuracy of a material’s elastic properties can be increased if an “incorrect” set of elastic properties is removed from the identification process. The research covers only numerical research, with a physical experiment simulation.

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Section: Introductionmentioning

confidence: 99%

Objective Function Distortion Reduction in Identification Technique of Composite Material Elastic Properties

Ragauskas,

Jasevičius

2024

Vibration

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“…The principle of the method discussed in the current paper is based on the algorithm of the clustering of multivariate data series obtained as a result of the application of the MPM to the experimental data. In the proposed technique, multi-objective optimization is employed, which is usually used to improve the accuracy of particular-parameter identification [25]. At the first stage, the computationally efficient method based on the calculation of the Fourier transform of Green's matrix (GMM) is employed (see [26] and references therein) iteratively, and the obtained solution is used for the filter construction with a decreasing bandwidth, which allows us to obtain nearly noise-free classified data (with mode separation).…”

Section: Introductionmentioning

confidence: 99%

Improved Unsupervised Learning Method for Material-Properties Identification Based on Mode Separation of Ultrasonic Guided Waves

et al. 2022

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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.

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“…The principle of the method discussed in the current paper is based on the algorithm of the clustering of multivariate data series obtained as a result of the application of the MPM to the experimental data. In the proposed technique, multi-objective optimization is employed, which is usually used to improve the accuracy of particular parameter identification [23]. At the first stage, the computationally efficient method based on the calculation of the Fourier transform of Green's matrix (GMM) is employed (see [24] and references therein) 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).…”

Section: Introductionmentioning

confidence: 99%

Improved Unsupervised Learning Method for Material Properties Identification Based on Mode Separation of Ultrasonic Guided Waves

Golub¹,

Doroshenko²,

Arsenov³

et al. 2022

Preprint

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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.

show abstract

Identification of elastic properties utilizing non-destructive vibrational evaluation methods with emphasis on definition of objective functions: a review

Cited by 16 publications

References 94 publications

Objective Function Distortion Reduction in Identification Technique of Composite Material Elastic Properties

Objective Function Distortion Reduction in Identification Technique of Composite Material Elastic Properties

Improved Unsupervised Learning Method for Material-Properties Identification Based on Mode Separation of Ultrasonic Guided Waves

Improved Unsupervised Learning Method for Material Properties Identification Based on Mode Separation of Ultrasonic Guided Waves

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