Ultrasonic imaging of defects in coarse-grained steels with the decomposition of the time reversal operator

Villaverde, Eduardo Lopez; Robert, Sébastien; Prada, Claire

doi:10.1121/1.4958683

Cited by 23 publications

(8 citation statements)

References 17 publications

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“…TFM uses this matrix to create images that lend themselves to a relatively easy interpretation by both human and artificial intelligence. However, TFM images are often contaminated by noise and various strategies have been offered to modify the TFM algorithm to eliminate false indications [7,8] and reduce noise [8][9][10], enabling real-time imaging with portable NDT devices [8,11]. Researchers also began to explore application of machine learning to NDT [12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Towards Explainable Augmented Intelligence (AI) for Crack Characterization

Fradkin¹,

Altinbasak²,

Darmon

2021

Applied Sciences

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Crack characterization is one of the central tasks of NDT&E (the Non-destructive Testing and Evaluation) of industrial components and structures. These days data necessary for carrying out this task are often collected using ultrasonic phased arrays. Many ultrasonic phased array inspections are automated but interpretation of the data they produce is not. This paper offers an approach to designing an explainable AI (Augmented Intelligence) to meet this challenge. It describes a C code called AutoNDE, which comprises a signal-processing module based on a modified total focusing method that creates a sequence of two-dimensional images of an evaluated specimen; an image-processing module, which filters and enhances these images; and an explainable AI module—a decision tree, which selects images of possible cracks, groups those of them that appear to represent the same crack and produces for each group a possible inspection report for perusal by a human inspector. AutoNDE has been trained on 16 datasets collected in a laboratory by imaging steel specimens with large smooth planar notches, both embedded and surface-breaking. It has been tested on two other similar datasets. The paper presents results of this training and testing and describes in detail an approach to dealing with the main source of error in ultrasonic data—undulations in the specimens’ surfaces.

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Section: Introductionmentioning

confidence: 99%

Towards Explainable Augmented Intelligence (AI) for Crack Characterization

Fradkin¹,

Altinbasak²,

Darmon

2021

Applied Sciences

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“…In effect, the power of the proposed method is based on the possibility of the rapid extraction of the local array data, which could be post-processed for different purposes. For example, additional signal processing might include imaging enhancement via eigendecomposition of the time reversal operator [39], [40], or to estimate the rate of single and multiple scattering in the data [38].…”

Section: Discussionmentioning

confidence: 99%

Optimal Extraction of Ultrasonic Scattering Features in Coarse Grained Materials

Villaverde

Croxford

Velichko

2021

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

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Ultrasonic array imaging is used in non-destructive testing for detection and characterisation of defects. The scattering behaviour of any feature can be described by a matrix of scattering coefficients, called the scattering matrix. This information is used for characterisation, and contrary to imagebased analysis, the scattering matrix allows characterisation of defects at the subwavelength scale. However, the defect scattering coefficients are in practice contaminated by other nearby scatterers or significant structural noise. In this context, an optimal procedure to extract scattering features from a selected region of interest in a beamformed image is here investigated. This work proposes two main strategies to isolate a target scatterer in order to recover exclusively the time responses of the desired scatterer.In this paper such strategies are implemented in delay-and-sum and frequency-wavenumber forms, and optimised to maximise the extraction rate. An experimental case in a polycrystalline material shows that the suggested procedures provide a rich frequency spectrum of the scattering matrix and are readily suited to minimise the effects of surrounding scattering noise. In doing so, the ability to deploy imaging methods that rely on the scattering matrix is enabled.

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“…The method was successfully applied to analyze a variety of materials with high noise levels, indicating that DOTR could significantly reduce the structural noise in the signal and retain the useful signal that overlapped the scattered noise signal band [ 17 , 18 ]. The key to the noise-reduction method is to determine the number of singular values of useful signal and noise signal, but a determination method of the number of singular values is not available [ 19 ]. Higher-order singular value decomposition (HOSVD), known as Tucker decomposition, is a multilinear extension of the concept of SVD and is commonly used in tensor decomposition [ 20 ].…”

Section: Introductionmentioning

confidence: 99%

An Ultrasonic Reverse Time Migration Imaging Method Based on Higher-Order Singular Value Decomposition

Zhang

Gao

Zhang

et al. 2022

Sensors

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An ultrasonic reverse time migration imaging method, based on high-order singular value decomposition, is proposed in the study to solve the problems of low signal-to-noise ratio (SNR) and excessive artifacts in defect ultrasonic detection imaging results of materials with high noise levels. In this method, based on the 3D structural properties of the ultrasonic full-matrix capture data, higher-order singular value decomposition is directly performed with the 3D data. The method overcomes the difficulty in selecting the number of singular values in the original singular value decomposition noise-reduction algorithm and realizes the one-step noise reduction processing of all the signals. Subsequently, the reverse time migration imaging is performed in the frequency domain, and high-quality acoustic images are obtained. The effects of the number of array elements, the center frequency of the excitation signal, and the number of defects on the denoising effect of the algorithm are investigated. It was experimentally demonstrated that the method could suppress the interference of noise signals and significantly improve the imaging SNR compared with total focusing method and the reverse time migration.

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Ultrasonic imaging of defects in coarse-grained steels with the decomposition of the time reversal operator

Cited by 23 publications

References 17 publications

Towards Explainable Augmented Intelligence (AI) for Crack Characterization

Towards Explainable Augmented Intelligence (AI) for Crack Characterization

Optimal Extraction of Ultrasonic Scattering Features in Coarse Grained Materials

An Ultrasonic Reverse Time Migration Imaging Method Based on Higher-Order Singular Value Decomposition

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