Quantitative detection of surface defect using laser-generated Rayleigh wave with broadband local wavenumber estimation

He, Jiayi; Liu, Xuekun; Cheng, Qichao; Yang, Shixi; Li, Miaoshuo

doi:10.1016/j.ultras.2023.106983

Cited by 9 publications

(3 citation statements)

References 41 publications

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“…Jun He and colleagues presented a quantitative approach for detecting surface anomalies using Rayleigh waves generated by lasers. Experimental results demonstrate that this method outperforms standing wave energy or reflected wave energy techniques, particularly in imaging vertical and inclined defects [55]. Similar challenges are also encountered in ultrasonic phased array testing.…”

Section: Conventional Surface Inspection Methodsmentioning

confidence: 76%

“…Owing to high maintenance costs and factors like surface roughness, speed, and environmental vibrations, LUGWT is predominantly used in precision industries, such as aerospace for defect detection, and remains largely experimental in pipeline applica-tions [65]. For instance, JH et al [55] utilized laser-generated guided waves to assess pipeline corrosion and successfully evaluated the location and size of defects in twodimensional scan images.…”

Section: Air Coupled Ultrasonic-guided Wave Testingmentioning

confidence: 99%

See 1 more Smart Citation

Systematic Evaluation of Ultrasonic In-Line Inspection Techniques for Oil and Gas Pipeline Defects Based on Bibliometric Analysis

Huang,

Chen,

et al. 2024

Sensors

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The global reliance on oil and gas pipelines for energy transportation is increasing. As the pioneering review in the field of ultrasonic defect detection for oil and gas pipelines based on bibliometric methods, this study employs visual analysis to identify the most influential countries, academic institutions, and journals in this domain. Through cluster analysis, it determines the primary trends, research hotspots, and future directions in this critical field. Starting from the current global industrial ultrasonic in-line inspection (ILI) detection level, this paper provides a flowchart for selecting detection methods and a table for defect comparison, detailing the comparative performance limits of different detection devices. It offers a comprehensive perspective on the latest ultrasonic pipeline detection technology from laboratory experiments to industrial practice.

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Section: Conventional Surface Inspection Methodsmentioning

confidence: 76%

Section: Air Coupled Ultrasonic-guided Wave Testingmentioning

confidence: 99%

Systematic Evaluation of Ultrasonic In-Line Inspection Techniques for Oil and Gas Pipeline Defects Based on Bibliometric Analysis

Huang,

Chen,

et al. 2024

Sensors

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show abstract

“…The Rayleigh wave is well suited to the evaluation of surface defects due to its high sensitivity to surface defects [8][9][10]. The defects can be detected and evaluated based on changes in the signal-noise ratio within echo signals, specifically the amplitude variation in Rayleigh waves [11].…”

Section: Introductionmentioning

confidence: 99%

Depth Evaluation of Tiny Defects on or near Surface Based on Convolutional Neural Network

Fei,

Cao,

et al. 2023

Applied Sciences

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This paper proposes a method for the detection and depth assessment of tiny defects in or near surfaces by combining laser ultrasonics with convolutional neural networks (CNNs). The innovation in this study lies in several key aspects. Firstly, a comprehensive analysis of changes in ultrasonic signal characteristics caused by variations in defect depth is conducted in both the time and frequency domains, based on discrete frequency spectra and original A—scan signals. Continuous wavelet transform (CWT) is employed to obtain wavelet time–frequency maps, demonstrating the consistent characteristics of this image with crack depth variations. A crucial innovation in this research involves the targeted design and optimization of the model based on the characteristics of ultrasonic signals and dataset size. This includes aspects such as data preparation, CNN architecture construction, and hyperparameter selection. The model is tested using a random validation set, which effectively demonstrates the CNN model’s validity and high precision. The proposed method enables the recognition and depth assessment of tiny defects on or near surfaces.

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Photoacoustic interaction mechanism with surface defects in freeform surface materials

Qin,

Li,

Chen

et al. 2024

Applied Acoustics

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Quantitative detection of surface defect using laser-generated Rayleigh wave with broadband local wavenumber estimation

Cited by 9 publications

References 41 publications

Systematic Evaluation of Ultrasonic In-Line Inspection Techniques for Oil and Gas Pipeline Defects Based on Bibliometric Analysis

Systematic Evaluation of Ultrasonic In-Line Inspection Techniques for Oil and Gas Pipeline Defects Based on Bibliometric Analysis

Depth Evaluation of Tiny Defects on or near Surface Based on Convolutional Neural Network

Photoacoustic interaction mechanism with surface defects in freeform surface materials

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