Guided Waves in Plates and Their Use in Materials Characterization

1997 ◽  
Vol 50 (5) ◽  
pp. 247-284 ◽  
Author(s):  
D. E. Chimenti
Keyword(s):  
Guided Waves ◽  
Large Body ◽  
Review Article ◽  
Guided Wave ◽  
Materials Characterization ◽  
Current State ◽  
Ultrasonic Characterization ◽  
Characterization Of Materials ◽  
Wave Methods

In this review article, the ultrasonic characterization of materials using guided plate waves and their usage to elucidate mechanical properties of plate-like structures is reviewed. The purpose here is to summarize and explain the large body of theoretical and experimental work in this developing field. It is also to gain a perspective on recent salient contributions and to analyze the current state of knowledge and practice in guided wave ultrasonics. Models of waves in plates are examined, as are the means to generate and detect them. Their application to several problems of current interest in materials characterization is treated in detail. In particular, composite materials and their inspection and characterization have been a major impetus in the development of guided wave methods. Techniques to inspect composites sensitively and reliably for defects and to probe their micromechanical behavior are a major focus of this article. Also considered are the characterization of adhesive bonds, the measurement of stress and texture, and the detection of defects using guided waves. This review article contains 362 references.

Stress evaluation in seven-wire strands based on singular value feature of ultrasonic guided waves

2021 ◽  
pp. 147592172110053
Author(s):  
Qian Ji ◽  
Li Jian-Bin ◽  
Liu Fan-Rui ◽  
Zhou Jian-Ting ◽  
Wang Xu
Keyword(s):  
Dispersion Curve ◽  
Support Vector Regression ◽  
Stress Level ◽  
Guided Waves ◽  
Guided Wave ◽  
Singular Value ◽  
Support Vector ◽  
Support Vector Regression Model ◽  
Various Boundary Conditions ◽  
Wave Methods

The seven-wire strands are the crucial components of prestressed structures, though their performance inevitably degrades with the passage of time. The ultrasonic guided wave methods have been intensely studied, owing to its tremendous potential for full-scale applications, among the existing nondestructive testing methods, for evaluating the stress status of strands. We have employed the theoretical and finite element methods to solve the dispersion curve of single wire and steel strands under various boundary conditions. Thereafter, the singular value decomposition was adopted to work with the simulated and experimental signals for extracting a feature vector that carries valuable stress status information. The effectiveness of the vector was verified by analyzing the relationship between the vector and the stress level. The vector was also used as an input to establish a support vector regression model. The accuracy of the model has been discussed for different sample sizes. The results show that the fundamental mode dispersion curve offset on the high-frequency part and cut-off frequency increases as the boundary constraints enhance. Simulated and experimental results have demonstrated the effectiveness and potential of the proposed support vector regression method for evaluating the stress level in the strands. This method performs well even at low stress levels and the reliability can be enhanced by adding more samples.

scholarly journals Learning Representations of Inorganic Materials from Generative Adversarial Networks

Symmetry ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 1889
Author(s):  
Tiantian Hu ◽  
Hui Song ◽  
Tao Jiang ◽  
Shaobo Li
Keyword(s):  
Large Scale ◽  
Inorganic Materials ◽  
Molecular Composition ◽  
Materials Characterization ◽  
Generative Adversarial Networks ◽  
Feature Engineering ◽  
Material Research ◽  
Generative Adversarial Network ◽  
Characterization Of Materials

The two most important aspects of material research using deep learning (DL) or machine learning (ML) are the characteristics of materials data and learning algorithms, where the proper characterization of materials data is essential for generating accurate models. At present, the characterization of materials based on the molecular composition includes some methods based on feature engineering, such as Magpie and One-hot. Although these characterization methods have achieved significant results in materials research, these methods based on feature engineering cannot guarantee the integrity of materials characterization. One possible approach is to learn the materials characterization via neural networks using the chemical knowledge and implicit composition rules shown in large-scale known materials. This article chooses an adversarial method to learn the composition of atoms using the Generative Adversarial Network (GAN), which makes sense for data symmetry. The total loss value of the discriminator on the test set is reduced from 4.1e13 to 0.3194, indicating that the designed GAN network can well capture the combination of atoms in real materials. We then use the trained discriminator weights for material characterization and predict bandgap, formation energy, critical temperature (Tc) of superconductors on the Open Quantum Materials Database (OQMD), Materials Project (MP), and SuperCond datasets. Experiments show that when using the same predictive model, our proposed method performs better than One-hot and Magpie. This article provides an effective method for characterizing materials based on molecular composition in addition to Magpie, One-hot, etc. In addition, the generator learned in this study generates hypothetical materials with the same distribution as known materials, and these hypotheses can be used as a source for new material discovery.

scholarly journals Applied Sciences Special Issue: Ultrasonic Guided Waves

2019 ◽  
Vol 9 (18) ◽  
pp. 3869 ◽  
Author(s):  
Clifford J. Lissenden
Keyword(s):  
Applied Sciences ◽  
Scientific Inquiry ◽  
Guided Waves ◽  
Ultrasonic Guided Waves ◽  
Special Issue ◽  
Practical Applications ◽  
Material State ◽  
Characterization Of Materials ◽  
Nondestructive Characterization

The propagation of ultrasonic guided waves in solids is an important area of scientific inquiry due primarily to their practical applications for the nondestructive characterization of materials, such as nondestructive inspection, quality assurance testing, structural health monitoring, and for achieving material state awareness [...]

Noncontact/Remote Material Characterization Using Ultrasonic Guided Wave Methods

2020 ◽  
Author(s):  
Zhaoyun Ma ◽  
Lingyu Yu
Keyword(s):  
Guided Waves ◽  
Broad Band ◽  
Laser Doppler Vibrometer ◽  
Pulsed Laser ◽  
Guided Wave ◽  
Target Plate ◽  
Ultrasonic Guided Wave ◽  
Time Space ◽  
Aluminum Plates ◽  
Wave Methods

Abstract Noncontact and remote NDE systems and methods are highly desired in a broad range of engineering applications such as material property characterization. This paper aims to develop such a noncontact/remote NDE system based on laser ultrasonic guided waves and establish its fundamental capability for material thickness evaluation. The noncontact system employs pulsed laser (PL) for guided wave actuation and scanning laser Doppler vibrometer (SLDV) for guided wave wavefield sensing. A cylindrical planoconvex lens is adopted to focus the pulsed laser beam to a line source in order to excite broad band signals in the target plate. Aluminum plates with different thicknesses are evaluated through SLDV line scans and 2D time-space wavefields are acquired. Frequency-wavenumber (f-k) spectra are obtained through 2D Fourier transform, and the A0 dispersion curve for each plate is extracted. Through Comparing the extracted A0 curve with the theoretical A0 dispersion curves, the thicknesses of the tested plates are identified. Reflective tape effect on the plates are also studied: the reflective tape attached for SLDV enhancement affects the guided waves in the target plate significantly when the plate is relatively thin.

The Characterization of Guided Waves on Hollow Cylinder From Non-Axisymmetric End Loading

2005 ◽  
Author(s):  
Longtao Li ◽  
Cunfu He ◽  
Bin Wu ◽  
Ying Li ◽  
Xiuyan Wang
Keyword(s):  
Hollow Cylinder ◽  
Cross Section ◽  
Guided Waves ◽  
Axial Direction ◽  
Guided Wave ◽  
Ultrasonic Guided Waves ◽  
Steel Pipe ◽  
Rapid Testing ◽  
Middle Point

Ultrasonic guided waves are used for the rapid testing of a steel pipe (O.D 70 mm, I.D 63 mm, 2544 mm long). The non-axisymmetric transducer ring (arc) is put on one end of the pipe to excite and receive the guided wave in the pipe. An artificial hole of 1 mm diameter can not be found by conventional axisymmetric end loading transducer. However, the non-axisymmetric transducer ring (arc), compared with the axisymmetric transducer ring, is very sensitive to the artificial hole when The middle point (MP) of the transducer arcs coincided with the center of the artificial hole on the cross section of the pipe. The results show that the non-axisymmetric end loading technology can locate the crack or defect on the pipe not only in the axial direction but also in the circumferential direction.

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