Characterisation of small embedded two-dimensional defects using multi-view Total Focusing Method imaging algorithm

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

doi:10.1016/j.ndteint.2021.102413

Characterisation of small embedded two-dimensional defects using multi-view Total Focusing Method imaging algorithm

Nicolas S Budyn, Anthony J Croxford, Rhodri L T Bevan, Jie Zhang, Paul D Wilcox^*

^*Corresponding author for this work

Research output: Contribution to journal › Article (Academic Journal) › peer-review

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Abstract

Defect characterisation is an essential step of nondestructive testing which aims to determine the shape and the size of a defect once detected. The imaging algorithm multi-view Total Focusing Method (TFM) has been successfully used to accurately size defects of size greater than several times the wavelength. However, characterising smaller defects remains difficult due to the diffraction limit. This paper introduces a probabilistic characterisation technique where the unknown defect is compared against a database of reference scatterers obtained
with an ultrasonic model. The measured TFM amplitudes act as a defect signature. A Bayesian framework is used to retrieve the reference defects which most credibly explain the measurements, and to calculate uncertainties.

Original language	English
Article number	102413
Number of pages	11
Journal	NDT and E International
Volume	119
Early online date	2 Feb 2021
DOIs	https://doi.org/10.1016/j.ndteint.2021.102413
Publication status	Published - 1 Apr 2021

Bibliographical note

Funding Information:
ACKNOWLEDGEMENTS The authors acknowledge financial support from the Department of Health via the National Institute for Health Research (NIHR) comprehensive Biomedical Research Centre at Guy's & St Thomas' NHS Foundation Trust in partnership with King's College London and King's College Hospital NHS Foundation Trust. The authors also acknowledge the support of the MRC Centre for Transplantation. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health.

Funding Information:
The authors acknowledge financial support from the Department of Health via the National Institute for Health Research (NIHR) comprehensive Biomedical Research Centre at Guy?s & St Thomas? NHS Foundation Trust in partnership with King?s College London and King?s College Hospital NHS Foundation Trust. The authors also acknowledge the support of the MRC Centre for Transplantation. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health.

Funding Information:
CONFLICTS OF INTEREST Veeru Kasivisvanathan is funded by a Doctoral Research Fellowship from the National Institute for Health Research. The views expressed in this publication are those of the author(s) and not necessarily those of the NHS, the National Institute for Health Research or the Department of Health.

Publisher Copyright:
© 2021 Elsevier Ltd

Keywords

Defect characterisation
Ultrasonic array
Ultrasonic imaging

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10.1016/j.ndteint.2021.102413

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title = "Characterisation of small embedded two-dimensional defects using multi-view Total Focusing Method imaging algorithm",

abstract = "Defect characterisation is an essential step of nondestructive testing which aims to determine the shape and the size of a defect once detected. The imaging algorithm multi-view Total Focusing Method (TFM) has been successfully used to accurately size defects of size greater than several times the wavelength. However, characterising smaller defects remains difficult due to the diffraction limit. This paper introduces a probabilistic characterisation technique where the unknown defect is compared against a database of reference scatterers obtainedwith an ultrasonic model. The measured TFM amplitudes act as a defect signature. A Bayesian framework is used to retrieve the reference defects which most credibly explain the measurements, and to calculate uncertainties.",

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note = "Funding Information: ACKNOWLEDGEMENTS The authors acknowledge financial support from the Department of Health via the National Institute for Health Research (NIHR) comprehensive Biomedical Research Centre at Guy's & St Thomas' NHS Foundation Trust in partnership with King's College London and King's College Hospital NHS Foundation Trust. The authors also acknowledge the support of the MRC Centre for Transplantation. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health. Funding Information: The authors acknowledge financial support from the Department of Health via the National Institute for Health Research (NIHR) comprehensive Biomedical Research Centre at Guy?s & St Thomas? NHS Foundation Trust in partnership with King?s College London and King?s College Hospital NHS Foundation Trust. The authors also acknowledge the support of the MRC Centre for Transplantation. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health. Funding Information: CONFLICTS OF INTEREST Veeru Kasivisvanathan is funded by a Doctoral Research Fellowship from the National Institute for Health Research. The views expressed in this publication are those of the author(s) and not necessarily those of the NHS, the National Institute for Health Research or the Department of Health. Publisher Copyright: {\textcopyright} 2021 Elsevier Ltd",

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AU - Wilcox, Paul D

N1 - Funding Information: ACKNOWLEDGEMENTS The authors acknowledge financial support from the Department of Health via the National Institute for Health Research (NIHR) comprehensive Biomedical Research Centre at Guy's & St Thomas' NHS Foundation Trust in partnership with King's College London and King's College Hospital NHS Foundation Trust. The authors also acknowledge the support of the MRC Centre for Transplantation. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health. Funding Information: The authors acknowledge financial support from the Department of Health via the National Institute for Health Research (NIHR) comprehensive Biomedical Research Centre at Guy?s & St Thomas? NHS Foundation Trust in partnership with King?s College London and King?s College Hospital NHS Foundation Trust. The authors also acknowledge the support of the MRC Centre for Transplantation. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health. Funding Information: CONFLICTS OF INTEREST Veeru Kasivisvanathan is funded by a Doctoral Research Fellowship from the National Institute for Health Research. The views expressed in this publication are those of the author(s) and not necessarily those of the NHS, the National Institute for Health Research or the Department of Health. Publisher Copyright: © 2021 Elsevier Ltd

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N2 - Defect characterisation is an essential step of nondestructive testing which aims to determine the shape and the size of a defect once detected. The imaging algorithm multi-view Total Focusing Method (TFM) has been successfully used to accurately size defects of size greater than several times the wavelength. However, characterising smaller defects remains difficult due to the diffraction limit. This paper introduces a probabilistic characterisation technique where the unknown defect is compared against a database of reference scatterers obtainedwith an ultrasonic model. The measured TFM amplitudes act as a defect signature. A Bayesian framework is used to retrieve the reference defects which most credibly explain the measurements, and to calculate uncertainties.

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Characterisation of small embedded two-dimensional defects using multi-view Total Focusing Method imaging algorithm

Abstract

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Improving Inspection Reliability through Data Fusion of Multi-View Array Data

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Characterisation of small embedded two-dimensional defects using multi-view Total Focusing Method imaging algorithm

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Improving Inspection Reliability through Data Fusion of Multi-View Array Data

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