A Model for Multiview Ultrasonic Array Inspection of Small Two-Dimensional Defects

Budyn, Nicolas; Bevan, Rhodri L. T.; Zhang, Jie; Croxford, Anthony J.; Wilcox, Paul D.

doi:10.1109/tuffc.2019.2909988

Cited by 38 publications

(30 citation statements)

References 33 publications

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“…A quantitative numerical comparison of the performance of candidate data fusion methods was performed prior to testing them on ultrasonic data. Example defect sensitivities, | , were produced using a single-frequency, ray-based forward model [13] for = 49 candidate defects located at the centre of the ROI shown in Fig. 1(a).…”

Section: Simulation Of Data For Comparing Fusion Methodsmentioning

confidence: 99%

Fusion of multi-view ultrasonic data for increased detection performance in non-destructive evaluation

et al. 2020

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State-of-the-art ultrasonic non-destructive evaluation (NDE) uses an array to rapidly generate multiple, information-rich views at each test position on a safety-critical component. However, the information for detecting potential defects is dispersed across views, and a typical inspection may involve thousands of test positions. Interpretation requires painstaking analysis by a skilled operator. In this paper, various methods for fusing multi-view data are developed. Compared with any one single view, all methods are shown to yield significant performance gains, which may be related to the general and edge cases for NDE. In the general case, a defect is clearly detectable in at least one individual view, but the view(s) depends on the defect location and orientation. Here, the performance gain from data fusion is mainly the result of the selective use of information from the most appropriate view(s) and fusion provides a means to substantially reduce operator burden. The edge cases are defects that cannot be reliably detected in any one individual view without false alarms. Here, certain fusion methods are shown to enable detection with reduced false alarms. In this context, fusion allows NDE capability to be extended with potential implications for the design and operation of engineering assets.

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Section: Simulation Of Data For Comparing Fusion Methodsmentioning

confidence: 99%

Fusion of multi-view ultrasonic data for increased detection performance in non-destructive evaluation

et al. 2020

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“…Not only does this increased viewing range make a small defect more likely to be seen, but it also enables a large defect to be examined from multiple angles, with features which may be invisible due to an insignificant reflection (or shadowing from a different part of the defect) in one view appearing in another. The terminology used to represent the different ray paths and wave modes (transverse, T , and longitudinal, L) is taken from [13], [16]. Views can be considered as direct, half-skip and full-skip, as shown in figure 2.…”

Section: (I)mentioning

confidence: 99%

“…Since the same holds true for the reception paths, this results in 36 combinations of transmission and reception, representing 36 views. However, due to reciprocity, only 21 are unique and only these 21 views are considered in this paper with the notation of [16]. A representative example of multi-view TFM images obtained from a single FMC dataset captured on a stainless steel sample (the full details of which are provided in section IV-A) is shown in figure 3.…”

Section: (I)mentioning

confidence: 99%

“…Defect modelling can be achieved using analytical models such as the commercial software CIVA [17]. Here, a singlefrequency ray-based model previously developed by the current authors is used [16]. This 2D based model incorporates directivity, transmission & reflection coefficients and beam-spread along with attenuation.…”

Section: Creation Of Input Data For Fusionmentioning

confidence: 99%

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Data Fusion of Multiview Ultrasonic Imaging for Characterization of Large Defects

Bevan

Budyn

Zhang

et al. 2020

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

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The multi-view total focusing method (TFM) enables a region of interest within a specimen to be imaged using different ray paths and wave mode combinations. For defects larger than the ultrasonic wavelength, different portions of the same defect may manifest in a number of views. For a crack, the tip diffraction response may be evident in certain views and the specular reflection in others. Accurate characterisation of large defects requires the information in multiple views to be combined. In this work, three data fusion methodologies are presented: a simple sum over all views, a sum weighted according to the inverse of the noise in each view and a matched filter approach. Four large defects are examined, one stress corrosion crack (SCC), two weld cracks and a pair of slagline defects in a weld. The matched filter (matched to a small circular void) provided significant improvement over the best individual view. The data fusion process incorporates artefact removal, where non-defect artefact signals within each image view are identified and masked, using a single defect-free dataset for training. The matched filter was able to accurately visualise the full 3D extent of the four defects, allowing characterisation via the decibel drop method. When compared to x-ray CT and micrograph data in the case of the SCC, the matched filter fusion provided excellent agreement. Its performance was also superior to any individual view while providing a single fused image that is easier for an operator to interpret than a set of multi-view images.

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“…The weights may also be chosen based on the signal statistics, an approach termed adaptive beamforming [12]- [14]. A different strategy to mitigate the influence of artefacts, when a specific defect type can be assumed, is to only consider views where the known defect has a significant amplitude [15], [16].…”

Section: Introductionmentioning

confidence: 99%

On the Use of the Geometric Median in Delay-and-Sum Ultrasonic Array Imaging

Budyn

2020

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

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Delay-and-sum algorithms are imaging techniques in non-destructive testing which form images by summing backpropagated signals. Under this approach, a small number of high intensity signals, such as those from boundary reflections, may create artefacts which degrade the image and hinder defect detection. This paper introduces a probabilistic model of the summation which explains the origin of this effect and proposes to replace the summation in the imaging algorithm by the more statistically robust geometric median. As demonstrated on a experimental inspection using multi-view Total Focusing Method and Plane Wave Imaging, this novel technique effectively suppresses some artefacts, at the expense of an increase of the structural noise amplitude and additional diffraction artefacts at the ends of some structural features. As such the geometric median provides an alternative imaging approach that may improve performance in some circumstances.

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A Model for Multiview Ultrasonic Array Inspection of Small Two-Dimensional Defects

Cited by 38 publications

References 33 publications

Fusion of multi-view ultrasonic data for increased detection performance in non-destructive evaluation

Fusion of multi-view ultrasonic data for increased detection performance in non-destructive evaluation

Data Fusion of Multiview Ultrasonic Imaging for Characterization of Large Defects

On the Use of the Geometric Median in Delay-and-Sum Ultrasonic Array Imaging

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