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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Materials Map under construction

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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1.080 Topics available

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977 Locations available

693.932 PEOPLE
693.932 People People

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Show results for 693.932 people that are selected by your search filters.

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Naji, M.
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Marshall, Stephen

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University of Strathclyde

in Cooperation with on an Cooperation-Score of 37%

Topics

Publications (12/12 displayed)

  • 2023Passive gamma-ray analysis of UO2 fuel rods using SrI2(Eu) scintillators in multi-detector arrangementscitations
  • 2022X-ray classification of Special Nuclear Materials using image segmentation and feature descriptorscitations
  • 2017Automated microstructural analysis of titanium alloys using digital image processing7citations
  • 2016Use of hyperspectral imaging for artwork authenticationcitations
  • 2015Detection and characterisation of the solar UV networkcitations
  • 2015Automated image stitching for fuel channel inspection of AGR corescitations
  • 2013Automated image stitching for enhanced visual inspections of nuclear power stationscitations
  • 2012A review of recent advances in the hit-or-miss transform12citations
  • 2011A fast method for computing the output of rank order filters within arbitrarily shaped windowscitations
  • 2007Restoration of star-field images using high-level languages and core librariescitations
  • 2006Advances in nonlinear signal and image processingcitations
  • 2005Texture classification of grey scale corrosion imagescitations

Places of action

Chart of shared publication
Joyce, Malcolm
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Taylor, James
1 / 11 shared
Parker, Andrew
1 / 3 shared
Bandala Sanchez, Manuel
1 / 1 shared
Zabalza, Jaime
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Murray, Paul
8 / 11 shared
Ma, Xiandong
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Cockbain, Neil
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Myres, Gareth
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Campbell, Andrew John
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Bernard, Robert
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Offin, Douglas
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Yakushina, Evgenia
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Ion, William
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Polak, Adam
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Stothard, D. J. M.
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Kelman, Timothy
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Eastaugh, F.
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Eastaugh, N.
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Paterson, Rhys L.
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Fletcher, Lyndsay
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Lynch, Chris
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West, Graeme
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Mcarthur, Stephen
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Ruet, C.
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Bruce, R.
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Devlin, M.
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Sicuranza, Giovanni. L.
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Gray, A.
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Mckenzie, J.
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Chart of publication period
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Co-Authors (by relevance)

  • Joyce, Malcolm
  • Taylor, James
  • Parker, Andrew
  • Bandala Sanchez, Manuel
  • Zabalza, Jaime
  • Murray, Paul
  • Ma, Xiandong
  • Cockbain, Neil
  • Myres, Gareth
  • Campbell, Andrew John
  • Bernard, Robert
  • Offin, Douglas
  • Yakushina, Evgenia
  • Ion, William
  • Polak, Adam
  • Stothard, D. J. M.
  • Kelman, Timothy
  • Eastaugh, F.
  • Eastaugh, N.
  • Paterson, Rhys L.
  • Fletcher, Lyndsay
  • Lynch, Chris
  • West, Graeme
  • Mcarthur, Stephen
  • Ruet, C.
  • Bruce, R.
  • Devlin, M.
  • Sicuranza, Giovanni. L.
  • Gray, A.
  • Mckenzie, J.
OrganizationsLocationPeople

article

Automated microstructural analysis of titanium alloys using digital image processing

  • Yakushina, Evgenia
  • Campbell, Andrew John
  • Marshall, Stephen
  • Murray, Paul
  • Ion, William
Abstract

<p>Titanium is a material that exhibits many desirable properties including a very high strength to weight ratio and corrosive resistance. However, the specific properties of any components depend upon the microstructure of the material, which varies by the manufacturing process. This means it is often necessary to analyse the microstructure when designing new processes or performing quality assurance on manufactured parts. For Ti6Al4V, grain size analysis is typically performed manually by expert material scientists as the complicated microstructure of the material means that, to the authors knowledge, no existing software reliably identifies the grain boundaries. This manual process is time consuming and offers low repeatability due to human error and subjectivity. In this paper, we propose a new, automated method to segment microstructural images of a Ti6Al4V alloy into its constituent grains and produce measurements. The results of applying this technique are evaluated by comparing the measurements obtained by different analysis methods. By using measurements from a complete manual segmentation as a benchmark we explore the reliability of the current manual estimations of grain size and contrast this with improvements offered by our approach.</p>

Topics
  • impedance spectroscopy
  • grain
  • grain size
  • strength
  • titanium
  • titanium alloy