Materials Map

Discover the materials research landscape. Find experts, partners, networks.

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The Materials Map is an open tool for improving networking and interdisciplinary exchange within materials research. It enables cross-database search for cooperation and network partners and discovering of the research landscape.

The dashboard provides detailed information about the selected scientist, e.g. publications. The dashboard can be filtered and shows the relationship to co-authors in different diagrams. In addition, a link is provided to find contact information.

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The Materials Map is still under development. In its current state, it is only based on one single data source and, thus, incomplete and contains duplicates. We are working on incorporating new open data sources like ORCID to improve the quality and the timeliness of our data. We will update Materials Map as soon as possible and kindly ask for your patience.

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University of Bristol

in Cooperation with on an Cooperation-Score of 37%

Topics

Publications (7/7 displayed)

  • 2022Sizing limitations of ultrasonic array images for non-sharp defects and their impact on structural integrity assessments3citations
  • 2020Data fusion of multi-view ultrasonic imaging for characterisation of large defects31citations
  • 2020Effect of crack-like defects on the fracture behaviour of Wire + Arc additively manufactured nickel-base Alloy 71876citations
  • 2012Monte Carlo inversion of ultrasonic array data to map anisotropic weld properties30citations
  • 2012Autofocus imagingcitations
  • 2010Ultrasonic condition monitoring using thin-film piezoelectric sensors11citations
  • 2006Monitoring of lubricant film failure in a ball bearing using ultrasound63citations

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Chart of shared publication
Bhat, Shivaprasad Shridhara
1 / 1 shared
Larrosa, Nicolas O.
1 / 21 shared
Bevan, Rhodri L. T.
1 / 1 shared
Budyn, Nicolas
1 / 1 shared
Kitazawa, So
1 / 1 shared
Croxford, Anthony J.
1 / 9 shared
Wilcox, Pd
3 / 20 shared
Coules, Harry E.
1 / 17 shared
Ding, Jialuo
1 / 39 shared
Williams, Stewart W.
1 / 33 shared
Jones, Cp
1 / 11 shared
Seow, Cui Er
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Wu, Guiyi
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Drinkwater, Bw
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Hunter, Alan J.
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Hunter, A.
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Hutson, D.
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Elgoyhen, J.
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Hood, Jp
1 / 1 shared
Kirk, Kj
1 / 1 shared
Dwyer-Joyce, Rs
2 / 3 shared
Chart of publication period
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2020
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Co-Authors (by relevance)

  • Bhat, Shivaprasad Shridhara
  • Larrosa, Nicolas O.
  • Bevan, Rhodri L. T.
  • Budyn, Nicolas
  • Kitazawa, So
  • Croxford, Anthony J.
  • Wilcox, Pd
  • Coules, Harry E.
  • Ding, Jialuo
  • Williams, Stewart W.
  • Jones, Cp
  • Seow, Cui Er
  • Wu, Guiyi
  • Drinkwater, Bw
  • Hunter, Alan J.
  • Hunter, A.
  • Hutson, D.
  • Elgoyhen, J.
  • Hood, Jp
  • Kirk, Kj
  • Dwyer-Joyce, Rs
OrganizationsLocationPeople

article

Data fusion of multi-view ultrasonic imaging for characterisation of large defects

  • Bevan, Rhodri L. T.
  • Budyn, Nicolas
  • Kitazawa, So
  • Croxford, Anthony J.
  • Wilcox, Pd
  • Zhang, Jie
Abstract

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.

Topics
  • impedance spectroscopy
  • crack
  • ultrasonic
  • void
  • stress corrosion