Nondestructive measurement of surface cracks using ultrasonic Rayleigh waves

Testa, A. J.

doi:10.31274/rtd-180813-8038

Cited by 3 publications

(7 citation statements)

References 52 publications

(23 reference statements)

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“…With the evolution of materials, increasingly complex and critical and critical nature of products, quality control and inspection methods become critically challenging [1]. Many methods were used by the industries to maintain the quality of their products and at the same time taking care the safety of their workers.…”

Section: Introductionmentioning

confidence: 99%

Non-Destructive Measurement and Evaluation of Surface Cracks Using Ultrasonic Rayleigh Waves – A Review

Hishamuddin

Mahzan

Yusup

2021

J. Phys.: Conf. Ser.

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Quality control and inspection methods have become a critical challenge in everyday situations of the engineering profession. This is due to the evolution of the materials used today in industry and also increasingly complex and critical nature of many of the products and structures produced with them. Ultrasonic measurement is widely used especially in oil and gas and aerospace industries. This method is used because it is effective and not involving damaging the original parts. In ultrasonic measurement there are few types of waves emitted and where one of it is Rayleigh wave or mostly known as surface wave. Surface waves are generated when longitudinal waves intersects a surface near to the second critical angle. This review paper will describe about the types of waves emitted and produce and also some of the research that has been done related to the surface wave. This research can contribute to green environment because it reduces waste by suggesting the uses of Perspex.

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Section: Introductionmentioning

confidence: 99%

Non-Destructive Measurement and Evaluation of Surface Cracks Using Ultrasonic Rayleigh Waves – A Review

Hishamuddin

Mahzan

Yusup

2021

J. Phys.: Conf. Ser.

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“…Singh [90] and Testa [95] Table 5.5 shows the cut-off wavelength Xc of different slit depths, receiver positions, energy levels and damping levels. The data are plotted in Fig.…”

Section: Slit Depth Predictionmentioning

confidence: 99%

“…Testa [95] used a 1.9 mm slit to check the influence of receiver position, he found that the cut-off wavelength was not affected by the receiver position. This is because the slit is shallow, so the RSRg wave is very strong and the influence of other waves seems very small.…”

Section: Slit Depth Predictionmentioning

confidence: 99%

See 2 more Smart Citations

The interactions of ultrasonic Rayleigh waves with surface discontinuities in steel

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Prediction of S‐wave velocity models from surface waves using deep learning

Cho,

Pyun,

Choi

et al. 2023

Near Surface Geophysics

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Surface wave (SW) methods extract dispersion properties of wavefields propagating through a seismic array (1D or 2D). This is achieved by analysing the phase velocity versus frequency (or wavelength) data. Afterwards, an inversion process is performed to construct near‐surface S‐wave velocity models. Among the SW methods, multichannel analysis of SWs (MASW) is commonly used for engineering applications, analysing dispersion characteristics by generating a dispersion image. However, classical MASW depends on the manual picking of dispersion curves, which can lead to subjective outcomes and require time and effort to obtain precise results. To avoid these pitfalls, many studies, including deep‐learning techniques, have focused on automating the process. Similarly, we propose a deep‐learning‐based algorithm that estimates the S‐wave velocity directly from the dispersion image of SWs. This algorithm consists of a convolutional neural network (CNN) designed to directly yield S‐wave velocity profiles and a fully connected network (multi‐layer perceptron) added to regularize predictions. Unlike typical SW techniques, the proposed approach does not incorporate prior information such as layer count and thickness. To ensure successful training, we modified the loss function to exploit the normalized mean squared error. The training dataset was generated by modelling synthetic shot gathers and transforming them into dispersion images for various 1D stratified velocity structures. After a sample is fed to the CNN network for inversion, the inversion network's output subsequently goes through an additional simple neural network (NN) to regularize the predicted S‐wave velocity model (which is the post‐processing step). The combined usage of deep‐learning‐based SW inversion with NN‐based post‐processing was assessed using test data. The proposed algorithm achieved an average relative error of approximately 7.49% in predicting the S‐wave velocity and was successfully applied to the field data. Additionally, we discuss its performance on noisy data as well as its applicability to out‐of‐training data. Numerical examples demonstrated that the proposed method is robust to noise, whereas it requires additional training to handle data beyond the distribution of the training data.

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Nondestructive measurement of surface cracks using ultrasonic Rayleigh waves

Cited by 3 publications

References 52 publications

Non-Destructive Measurement and Evaluation of Surface Cracks Using Ultrasonic Rayleigh Waves – A Review

Non-Destructive Measurement and Evaluation of Surface Cracks Using Ultrasonic Rayleigh Waves – A Review

The interactions of ultrasonic Rayleigh waves with surface discontinuities in steel

Prediction of S‐wave velocity models from surface waves using deep learning

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