The effect of distortion models on characterisation of real defects using ultrasonic arrays

Bai, Long; Velichko, Alexander; Clare, Adam T.; Dryburgh, Paul; Pieris, Don; Drinkwater, Bruce W.

doi:10.1016/j.ndteint.2020.102263

Cited by 10 publications

(2 citation statements)

References 31 publications

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“…[41]) is adopted. The Gaussian rough surface noise model has been applied to different measurement scenarios (e.g., characterisation of small machined notches and elliptical voids) for uncertainty quantification [44], and parameters of the noise model such as the RMS roughness and correlation lengths can be determined using a maximum-likelihood estimation approach [41]. In addition, as will be explained in Section III-A, the characterisation result (i.e., the posterior probability of the defect parameter) can be calculated analytically based on the general rough surface noise assumption.…”

Section: Description Of the Inversion Problemmentioning

confidence: 99%

Ultrasonic Defect Characterization Using the Scattering Matrix: A Performance Comparison Study of Bayesian Inversion and Machine Learning Schemas

Bai

Bourdais

Miorelli

et al. 2021

IEEE Trans. Ultrason., Ferroelect., Freq. Contr.

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Accurate defect characterisation is desirable in ultrasonic non-destructive evaluation as it can provide quantitative information about the defect type and geometry. For defect characterisation using ultrasonic arrays, high resolution images can provide the size and type information if a defect is relatively large. However, the performance of image-based characterisation becomes poor for small defects that are comparable to the wavelength. An alternative approach is to extract the far-field scattering coefficient matrix from the array data and use it for characterisation. Defect characterisation can be performed based on a scattering matrix database that consists of the scattering matrices of idealised defects with varying parameters. In this paper, the problem of characterising small surface-breaking notches is studied using two different approaches. The first approach is based on the introduction of a general coherent noise model, and it performs characterisation within the Bayesian framework. The second approach relies on a supervised machine learning (ML) schema based on a scattering matrix database, which is used as the training set to fit the ML model exploited for the characterisation task. It is shown that convolutional neural networks (CNNs) can achieve the best characterisation accuracy among the considered machine learning approaches and they give similar characterisation uncertainty to that of the Bayesian approach if a notch is favourably oriented. The performance of both approaches varied for unfavourably oriented notches, and the machine learning approach tends to give results with higher variance and lower biases.

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Section: Description Of the Inversion Problemmentioning

confidence: 99%

Ultrasonic Defect Characterization Using the Scattering Matrix: A Performance Comparison Study of Bayesian Inversion and Machine Learning Schemas

Bai

Bourdais

Miorelli

et al. 2021

IEEE Trans. Ultrason., Ferroelect., Freq. Contr.

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“…Given that ultrasonic crack inspections utilize the scattering phenomena at cracks, understanding the elastic-wave scattering at cracks is indispensable. Thus far, most studies on ultrasonic scattering at cracks have been limited to two dimensions 18 , 20 – 23 . On the other hand, an actual crack is composed of multiple scattering sources since crack faces have microscopic asperities 24 and parts of the faces are sometimes in contact 25 .…”

Section: Introductionmentioning

confidence: 99%

Exploring 3D elastic-wave scattering at interfaces using high-resolution phased-array system

Ohara

Remillieux

Ulrich

et al. 2022

Sci Rep

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The elastic-wave scattering at interfaces, such as cracks, is essential for nondestructive inspections, and hence, understanding the phenomenon is crucial. However, the elastic-wave scattering at cracks is very complex in three dimensions since microscopic asperities of crack faces can be multiple scattering sources. We propose a method for exploring 3D elastic-wave scattering based on our previously developed high-resolution 3D phased-array system, the piezoelectric and laser ultrasonic system (PLUS). We describe the principle of PLUS, which combines a piezoelectric transmitter and a 2D mechanical scan of a laser Doppler vibrometer, enabling us to resolve a crack into a collection of scattring sources. Subsequently, we show how the 3D elastic-wave scattering in the vicinity of each response can be extracted. Here, we experimentally applied PLUS to a fatigue-crack specimen. We found that diverse 3D elastic-wave scattering occurred in a manner depending on the responses within the fatigue crack. This is significant because access to such information will be useful for optimizing inspection conditions, designing ultrasonic measurement systems, and characterizing cracks. More importantly, the described methodology is very general and can be applied to not only metals but also other materials such as composites, concrete, and rocks, leading to progress in many fields.

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Resolution enhancement of ultrasonic imaging at oblique incidence by using WTFM based on FMC-AR

et al. 2021

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The effect of distortion models on characterisation of real defects using ultrasonic arrays

Cited by 10 publications

References 31 publications

Ultrasonic Defect Characterization Using the Scattering Matrix: A Performance Comparison Study of Bayesian Inversion and Machine Learning Schemas

Ultrasonic Defect Characterization Using the Scattering Matrix: A Performance Comparison Study of Bayesian Inversion and Machine Learning Schemas

Exploring 3D elastic-wave scattering at interfaces using high-resolution phased-array system

Resolution enhancement of ultrasonic imaging at oblique incidence by using WTFM based on FMC-AR

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