Materials Map

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

  • About
  • Privacy Policy
  • Legal Notice
  • Contact

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.

×

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.

To Graph

1.080 Topics available

To Map

977 Locations available

693.932 PEOPLE
693.932 People People

693.932 People

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

←

Page 1 of 27758

→
←

Page 1 of 0

→
PeopleLocationsStatistics
Naji, M.
  • 2
  • 13
  • 3
  • 2025
Motta, Antonella
  • 8
  • 52
  • 159
  • 2025
Aletan, Dirar
  • 1
  • 1
  • 0
  • 2025
Mohamed, Tarek
  • 1
  • 7
  • 2
  • 2025
Ertürk, Emre
  • 2
  • 3
  • 0
  • 2025
Taccardi, Nicola
  • 9
  • 81
  • 75
  • 2025
Kononenko, Denys
  • 1
  • 8
  • 2
  • 2025
Petrov, R. H.Madrid
  • 46
  • 125
  • 1k
  • 2025
Alshaaer, MazenBrussels
  • 17
  • 31
  • 172
  • 2025
Bih, L.
  • 15
  • 44
  • 145
  • 2025
Casati, R.
  • 31
  • 86
  • 661
  • 2025
Muller, Hermance
  • 1
  • 11
  • 0
  • 2025
Kočí, JanPrague
  • 28
  • 34
  • 209
  • 2025
Šuljagić, Marija
  • 10
  • 33
  • 43
  • 2025
Kalteremidou, Kalliopi-ArtemiBrussels
  • 14
  • 22
  • 158
  • 2025
Azam, Siraj
  • 1
  • 3
  • 2
  • 2025
Ospanova, Alyiya
  • 1
  • 6
  • 0
  • 2025
Blanpain, Bart
  • 568
  • 653
  • 13k
  • 2025
Ali, M. A.
  • 7
  • 75
  • 187
  • 2025
Popa, V.
  • 5
  • 12
  • 45
  • 2025
Rančić, M.
  • 2
  • 13
  • 0
  • 2025
Ollier, Nadège
  • 28
  • 75
  • 239
  • 2025
Azevedo, Nuno Monteiro
  • 4
  • 8
  • 25
  • 2025
Landes, Michael
  • 1
  • 9
  • 2
  • 2025
Rignanese, Gian-Marco
  • 15
  • 98
  • 805
  • 2025

Wilson, John W.

  • Google
  • 11
  • 25
  • 74

University of Manchester

in Cooperation with on an Cooperation-Score of 37%

Topics

Publications (11/11 displayed)

  • 2022Indirect yoke-based B-H hysteresis measurement method determining the magnetic properties of macroscopic ferromagnetic samples part I: Room temperature2citations
  • 2019Magnetic characterisation of grain size and precipitate distribution by major and minor BH loop measurements19citations
  • 2017Detection of creep degradation during pressure vessel testing using electromagnetic sensor technology5citations
  • 2017Optimized setup and protocol for magnetic domain imaging with in Situ hysteresis measurement4citations
  • 2016Defect representation using the electromagnetic tensor formulation for eddy current NDTcitations
  • 2016Defect representation using the electromagnetic tensor formulation for eddy current NDTcitations
  • 2015Electromagnetic evaluation of the microstructure of grade 91 tubes/pipes15citations
  • 2014Differential permeability behaviour of P9 and T22 power station Steels13citations
  • 2014Incremental permeability and magnetic Barkhausen noise for the assessment of microstructural changes in Grade 91 power station tubescitations
  • 2013Magnetic sensing for microstructural assessment of power station steels: Differential permeability and magnetic hysteresis2citations
  • 2010Sensor fusion for electromagnetic stress measurement and material characterisation14citations

Places of action

Chart of shared publication
Ebner, R.
1 / 6 shared
Mevec, D. G.
1 / 1 shared
Prevedel, P.
1 / 2 shared
Riedler, J. M.
1 / 1 shared
Raninger, P.
1 / 2 shared
Jászfi, V.
1 / 2 shared
Peyton, Antony J.
7 / 19 shared
Davis, Claire L.
3 / 7 shared
Liu, Jun
5 / 25 shared
Shibli, Ahmed
1 / 3 shared
Davis, Claire
3 / 47 shared
Allen, David J.
1 / 1 shared
Yin, Wuliang
4 / 9 shared
Karimian, Noushin
3 / 8 shared
Peyton, Anthony J.
2 / 11 shared
Lu, Mingyang
1 / 1 shared
Strangwood, Martin
1 / 19 shared
Parker, Jonathan
1 / 3 shared
Karimian, N.
2 / 8 shared
Kahlon, Navdeep Singh
1 / 1 shared
Liu, J.
1 / 87 shared
Davis, C. L.
1 / 15 shared
Morozov, Maxim
1 / 9 shared
Qubaa, Abd
1 / 1 shared
Tian, Gui
1 / 1 shared
Chart of publication period
2022
2019
2017
2016
2015
2014
2013
2010

Co-Authors (by relevance)

  • Ebner, R.
  • Mevec, D. G.
  • Prevedel, P.
  • Riedler, J. M.
  • Raninger, P.
  • Jászfi, V.
  • Peyton, Antony J.
  • Davis, Claire L.
  • Liu, Jun
  • Shibli, Ahmed
  • Davis, Claire
  • Allen, David J.
  • Yin, Wuliang
  • Karimian, Noushin
  • Peyton, Anthony J.
  • Lu, Mingyang
  • Strangwood, Martin
  • Parker, Jonathan
  • Karimian, N.
  • Kahlon, Navdeep Singh
  • Liu, J.
  • Davis, C. L.
  • Morozov, Maxim
  • Qubaa, Abd
  • Tian, Gui
OrganizationsLocationPeople

document

Defect representation using the electromagnetic tensor formulation for eddy current NDT

  • Yin, Wuliang
  • Peyton, Antony J.
  • Karimian, Noushin
  • Wilson, John W.
Abstract

<p>A difficulty for eddy current inspection is in the interpretation of data from either individual detectors or arrays. For individual detectors, it is common to monitor signal variations in the impedance plane and then detect signal variations outside a pre-defined envelope. For eddy current arrays (ECA), the data is often displayed as an image as the array is passed over the test piece. The image often has a qualitative grey / colour scale selected to emphasise anomalies caused by defects. Quantitative information such as crack sizing is often either based on calibrations of empirical responses from test pieces or relationships derived from much simplified analytic models. Quantitative eddy current inspection relies on an accurate model of the sensor system. This requires a solution to the full 3D eddy current problem describing the particular application. Unfortunately, standard techniques to solve eddy current problems, such as the finite element method (FEM) or boundary element method (BEM) or the method of auxiliary sources (MAS) are numerically intensive; and therefore, are typically used for off-line studies. This research aims to improve modelling techniques for eddy current inspection and inversion, and illustrate a different approach to modelling which is highly computationally efficient. This is achieved by representing the defect by its eddy current signature in the form of an equivalent tensor (a 3 by 3 matrix representing the X Y Z responses of the defect to each of the X Y Z components of the applied field) allowing the sensor response to be determined on-line with relatively modest computing hardware. These results will be used as a basis for the further development of an eddy current inspection system which can supply online quantitative information about defect depth and orientation using real time tensor calculations.</p>

Topics
  • impedance spectroscopy
  • crack