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 Strathclyde

in Cooperation with on an Cooperation-Score of 37%

Topics

Publications (6/6 displayed)

  • 2023SatelliteCloudGenerator14citations
  • 2020Composite laminate delamination detection using transient thermal conduction profiles and machine learning based data analysis6citations
  • 2020Defect detection in aerospace sandwich composite panels using conductive thermography and contact sensors11citations
  • 2020Non-destructive identification of fibre orientation in multi-ply biaxial laminates using contact temperature sensors4citations
  • 2019A novel methodology for macroscale, thermal characterization of carbon fiber-reinforced polymer for integrated aircraft electrical power systems2citations
  • 2019A novel methodology for macroscale, thermal characterization of carbon fiber-reinforced polymer for integrated aircraft electrical power systems2citations

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Chart of shared publication
Czerkawski, Mikolaj
1 / 1 shared
Michie, Walter
4 / 5 shared
Tachtatzis, Christos
6 / 8 shared
Gillespie, David
3 / 4 shared
Bellekens, Xavier
2 / 2 shared
Andonovic, Ivan
5 / 6 shared
Hamilton, Andrew
4 / 11 shared
Neilson, Brian
1 / 1 shared
Mckay, Ewan J.
1 / 1 shared
Burt, Graeme M.
1 / 3 shared
Cleary, Alison
2 / 2 shared
Jones, Catherine E.
1 / 3 shared
Norman, Patrick J.
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Hamilton, Andrew W.
1 / 1 shared
Galloway, Stuart J.
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Michie, Craig
1 / 1 shared
Norman, Patrick
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Jones, Catherine
1 / 5 shared
Galloway, Stuart
1 / 1 shared
Burt, Graeme
1 / 10 shared
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2020
2019

Co-Authors (by relevance)

  • Czerkawski, Mikolaj
  • Michie, Walter
  • Tachtatzis, Christos
  • Gillespie, David
  • Bellekens, Xavier
  • Andonovic, Ivan
  • Hamilton, Andrew
  • Neilson, Brian
  • Mckay, Ewan J.
  • Burt, Graeme M.
  • Cleary, Alison
  • Jones, Catherine E.
  • Norman, Patrick J.
  • Hamilton, Andrew W.
  • Galloway, Stuart J.
  • Michie, Craig
  • Norman, Patrick
  • Jones, Catherine
  • Galloway, Stuart
  • Burt, Graeme
OrganizationsLocationPeople

article

Composite laminate delamination detection using transient thermal conduction profiles and machine learning based data analysis

  • Gillespie, David
  • Bellekens, Xavier
  • Andonovic, Ivan
  • Michie, Walter
  • Atkinson, Robert
  • Hamilton, Andrew
  • Tachtatzis, Christos
Abstract

Delaminations within aerospace composites are of particular concern, presenting within composite laminate structures without visible surface indications. Transmission based thermography techniques using contact temperature sensors and surface mounted heat sources are able to detect reductions in thermal conductivity and in turn impact damage and large disbonds can be detected. However delaminations between Carbon Fibre Reinforced Polymer (CFRP) plies are not immediately discoverable using the technique. The use of transient thermal conduction profiles induced from zonal heating of a CFRP laminate to ascertain inter-laminate differences has been demonstrated and the paper builds on this method further by investigating the impact of inter laminate inclusions, in the form of delaminations, to the transient thermal conduction profile of multi-ply bi-axial CFRP laminates. Results demonstrate that as the distance between centre of the heat source and delamination increase, whilst maintaining the delamination within the heated area, the resultant transient thermal conduction profile is measurably different to that of a homogeneous region at the same distance. The method utilises a supervised Support Vector Classification (SVC) algorithm to detect delaminations using temperature data from either the edge of the defect or the centre during a 140 s ramped heating period to 80 ∘C. An F1 score in the classification of delaminations or no delamination at an overall accuracy of over 99% in both training and with test data separate from the training process has been achieved using data points effected by transient thermal conduction due to structural dissipation at 56.25 mm.

Topics
  • impedance spectroscopy
  • surface
  • polymer
  • Carbon
  • inclusion
  • composite
  • thermal conductivity
  • machine learning
  • thermography