Acousto-ultrasonic analysis of defects in composite specimens used in transportation domain

Spyrou, Evaggelos; Tsenis, Theocharis; Kappatos, Vassilios

doi:10.21595/jme.2021.21932

Cited by 6 publications

(4 citation statements)

References 20 publications

(20 reference statements)

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“…The commonly used methods for detecting defects in non-metallic composites are ultrasonic testing, radiographic testing, infrared testing, and microwave nondestructive testing [3][4][5][6][7]. Ultrasonic testing requires a coupling medium to contact the object being tested, which may cause damage to it, and this can be a limitation in certain situations.…”

Section: Introductionmentioning

confidence: 99%

Defects Detection Method Based on Programmable Spoof Surface Plasmon Polaritons in Non-Metallic Composites

Jin

Yang

et al. 2023

Micromachines

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Microwave nondestructive testing (NDT) offers promising application prospects due to its advantages of non-contact inspection in detecting defects in non-metallic composites. However, the detection sensitivity of this technology is generally affected by the lift-off effect. To reduce this effect and highly concentrate electromagnetic fields on defects, a defect detection method using scanning instead of moving sensors in the microwave frequency range was proposed. Additionally, a novel sensor based on the programmable spoof surface plasmon polaritons (SSPPs) was designed for non-destructive detection in non-metallic composites. The unit structure of the sensor was made up of a metallic strip and a split ring resonator (SRR). A varactor diode was loaded between the inner and outer rings of the SRR, and by changing the capacitance of this diode using electronic scanning, the field concentration phenomenon of the SSPPs sensor can be moved along a specific direction for defect detection. By using this proposed method and sensor, the location of a defect can be analyzed without moving the sensor. The experimental results demonstrated that the proposed method and designed SSPPs sensor can be effectively applied in detecting defects in non-metallic materials.

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Section: Introductionmentioning

confidence: 99%

Defects Detection Method Based on Programmable Spoof Surface Plasmon Polaritons in Non-Metallic Composites

Jin

Yang

et al. 2023

Micromachines

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“…Acousto-ultrasonics (AU) is a non-destructive approach and structural assessment method that integrates acoustic emission (AE) methods with ultrasonic excitation of stress waves [1]. AU is a type of inspection methodology that is used to detect and analyse defects and damage in structures [2]. AU can be defined as AE that has been simulated or facilitated by ultrasonic sources which is linked with AE methodology.…”

Section: Introductionmentioning

confidence: 99%

Determination of acousto-ultrasonic wave propagation in an isotropic aluminium Al6082-T6 plate

PRASAD,

Jombo,

Ismail

et al. 2023

Preprint

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The ability to guarantee reliable and damage-tolerant operation of structures is critical to their safe operation. Various non-destructive evaluation (NDE) techniques, such as ultrasonics, acoustic emission, thermography, acousto-ultrasonics (AU), among others could potentially provide a means of inspecting cracks, defects and damage responses in structures. The main aim of this study is to look into the physical problem of AU wave propagation through an isotropic plate and its analytical validation, using wave dispersion concept. The experimental investigation was conducted on aluminium Al6082-T6 alloy plate specimen. Wave propagation speed on top and bottom surfaces of the plate was investigated. The variation of the wave speed and attenuation with frequency were identified. From the results obtained experimentally, the wave dispersion was verified analytically. Interestingly, Lamb waves were discovered to be the dominating AU waves. The wave was appreciably non-dispersive in the frequency extent from 150 kHz to 1 MHz and the attenuation coefficient was increased with the frequency. This finding provides a significant breakthrough to the implementation of acousto- ultrasonics in the non- destructive testing applications, such as accessing the damage in composite structures, including wind turbine blades. AU has an active sensing mechanism, providing an edge over acoustic emission (AE) method which is a passive mode of damage sensing and detection, as external loading is needed for the damage sensing.

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“…The pipeline is a crucial asset for transporting different critical items such as oil, gas and water, therefore it plays a vital role in the reliable and safe operation of the city [1]. However, in the service process, they are inevitably subjected to various external factors, resulting in different types and degrees of defects on the pipeline surface [2]. With the continuous growth and deterioration of pipeline defects, the strength, pressure bearing capacity and corrosion resistance of the pipeline will continue to decline, and the service life of the pipeline will also be shortened [3].…”

Section: Introductionmentioning

confidence: 99%

A pipeline defect estimation method based on supervised learning fusion model

Li²,

Wang

et al. 2022

Preprint

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Pipeline health assessment is an important work in industry, and information on the type and size of defects is an essential basis for assessing the health of a pipeline. Therefore, a pipeline defect estimation method based on supervised learning ensemble model is proposed in this paper. Firstly, several typical feature factors are calculated using feature formulas in the field of acoustics, capable of distinguishing the defect signal variability. Thereafter, Pearson correlation coefficient analysis and Random Forest importance ranking feature analysis methods are utilized to filter out the more valuable features. To improve the performance of defect estimation, a fusion model combining qualitative and quantitative analysis based on Random Forest and XGBoost is constructed to preferentially identify the type of defect signal qualitatively and then predict the size quantitatively. Finally, experimental results and comprehensive analysis with other mainstream supervised learning methods indicate that the prediction error of this method is basically below 1.5%, which addresses the issue of the low estimation accuracy of traditional methods.

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Acousto-ultrasonic analysis of defects in composite specimens used in transportation domain

Cited by 6 publications

References 20 publications

Defects Detection Method Based on Programmable Spoof Surface Plasmon Polaritons in Non-Metallic Composites

Defects Detection Method Based on Programmable Spoof Surface Plasmon Polaritons in Non-Metallic Composites

Determination of acousto-ultrasonic wave propagation in an isotropic aluminium Al6082-T6 plate

A pipeline defect estimation method based on supervised learning fusion model

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