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

Loukas, Charalampos

  • Google
  • 13
  • 45
  • 82

University of Strathclyde

in Cooperation with on an Cooperation-Score of 37%

Topics

Publications (13/13 displayed)

  • 2023Flexible and automated robotic multi-pass arc weldingcitations
  • 2023In-process non-destructive evaluation of metal additive manufactured components at build using ultrasound and eddy-current approaches11citations
  • 2023In-process non-destructive evaluation of metal additive manufactured components at build using ultrasound and eddy-current approaches11citations
  • 2023Driving towards flexible and automated robotic multi-pass arc weldingcitations
  • 2022Autonomous and targeted eddy current inspection from UT feature guided wave screening of resistance seam weldscitations
  • 2022Multi-sensor electromagnetic inspection feasibility for aerospace composites surface defectscitations
  • 2022Collaborative robotic wire + arc additive manufacture and sensor-enabled in-process ultrasonic non-destructive evaluation16citations
  • 2022Automated multi-modal in-process non-destructive evaluation of wire + arc additive manufacturingcitations
  • 2022Targeted eddy current inspection based on ultrasonic feature guided wave screening of resistance seam weldscitations
  • 2022In-process non-destructive evaluation of wire + arc additive manufacture components using ultrasound high-temperature dry-coupled roller-probecitations
  • 2022Collaborative robotic Wire + Arc Additive Manufacture and sensor-enabled in-process ultrasonic Non-Destructive Evaluation16citations
  • 2022Automated real time eddy current array inspection of nuclear assets16citations
  • 2021A cost-function driven adaptive welding framework for multi-pass robotic welding12citations

Places of action

Chart of shared publication
Dobie, Gordon
4 / 21 shared
Gachagan, Anthony
7 / 76 shared
Sibson, Jim
3 / 3 shared
Jones, Richard
3 / 6 shared
Macleod, Charles N.
12 / 45 shared
Warner, Veronica
2 / 2 shared
Pierce, Stephen
8 / 51 shared
Halavage, Steven
6 / 6 shared
Mohseni, Ehsan
8 / 22 shared
Ding, Jialuo
5 / 39 shared
Williams, Stewart
6 / 39 shared
Rizwan, Muhammad Khalid
3 / 4 shared
Misael, Pimentel Espirindio E. Silva
5 / 5 shared
Mckegney, Scott
6 / 6 shared
Lines, David
8 / 18 shared
Wathavana Vithanage, Randika Kosala
5 / 11 shared
Foster, Euan A.
2 / 2 shared
Zimermann, Rastislav
6 / 9 shared
Fitzpatrick, Stephen
6 / 14 shared
Vasilev, Momchil
10 / 17 shared
Mohseni, Ehsan
2 / 4 shared
Pierce, Stephen Gareth
3 / 3 shared
Vithanage, Randika K. W.
2 / 2 shared
Mcinnes, Martin
3 / 3 shared
Foster, Euan
3 / 8 shared
Bernard, Robert
3 / 5 shared
Mcknight, Shaun
3 / 7 shared
Bolton, Gary
3 / 5 shared
Foster, E.
1 / 2 shared
Obrien-Oreilly, J.
1 / 3 shared
Munro, G.
1 / 3 shared
Ohare, T.
1 / 3 shared
Mcinnes, M.
1 / 2 shared
Burnham, K.
1 / 1 shared
Mcknight, S.
1 / 3 shared
Gover, H.
1 / 1 shared
Paton, S.
1 / 1 shared
Grosser, M.
1 / 2 shared
Dingv, Jialuo
1 / 1 shared
Misael Pimentel, Espirindio E. Silva
1 / 1 shared
Javadi, Yashar
2 / 31 shared
Macdonald, Charles
1 / 1 shared
Foster, Euan Alexander
1 / 1 shared
Nicolson, Ewan
1 / 5 shared
Williams, Veronica
1 / 1 shared
Chart of publication period
2023
2022
2021

Co-Authors (by relevance)

  • Dobie, Gordon
  • Gachagan, Anthony
  • Sibson, Jim
  • Jones, Richard
  • Macleod, Charles N.
  • Warner, Veronica
  • Pierce, Stephen
  • Halavage, Steven
  • Mohseni, Ehsan
  • Ding, Jialuo
  • Williams, Stewart
  • Rizwan, Muhammad Khalid
  • Misael, Pimentel Espirindio E. Silva
  • Mckegney, Scott
  • Lines, David
  • Wathavana Vithanage, Randika Kosala
  • Foster, Euan A.
  • Zimermann, Rastislav
  • Fitzpatrick, Stephen
  • Vasilev, Momchil
  • Mohseni, Ehsan
  • Pierce, Stephen Gareth
  • Vithanage, Randika K. W.
  • Mcinnes, Martin
  • Foster, Euan
  • Bernard, Robert
  • Mcknight, Shaun
  • Bolton, Gary
  • Foster, E.
  • Obrien-Oreilly, J.
  • Munro, G.
  • Ohare, T.
  • Mcinnes, M.
  • Burnham, K.
  • Mcknight, S.
  • Gover, H.
  • Paton, S.
  • Grosser, M.
  • Dingv, Jialuo
  • Misael Pimentel, Espirindio E. Silva
  • Javadi, Yashar
  • Macdonald, Charles
  • Foster, Euan Alexander
  • Nicolson, Ewan
  • Williams, Veronica
OrganizationsLocationPeople

document

Multi-sensor electromagnetic inspection feasibility for aerospace composites surface defects

  • Foster, E.
  • Dobie, Gordon
  • Loukas, Charalampos
  • Obrien-Oreilly, J.
  • Mohseni, Ehsan
  • Munro, G.
  • Ohare, T.
  • Mcinnes, M.
  • Burnham, K.
  • Macleod, Charles N.
  • Mcknight, S.
  • Gover, H.
  • Paton, S.
  • Wathavana Vithanage, Randika Kosala
  • Grosser, M.
  • Pierce, Stephen
Abstract

UK's presence at the forefront of composite manufacturing in Europe has never been more important provided how vital these structures are for i) slowing the climate change through reduction of fuel consumption and carbon footprint in different industries, and ii) development of wind and tidal blades to generate cleaner energy to achieve the net-zero target by the middle of the century. Therefore, the composite technology, Carbon Fibre Reinforced Polymers (CFRP) in particular, has been dominating the aerospace, energy, and defense industries, and this trend is expected to grow in the years to come. Non-Destructive Evaluation (NDE) is essential during manufacturing: to identify any defects early in the process as, if defects remain undetected, they could have far-reaching implications for the cost of scraped/repaired parts and the safety of final components, and ii) at later stages of manufacturing and post-manufacturing: to ensure the quality, integrity, and fitness for service of these safetycritical components. Although Ultrasound Testing (UT) has been predominantly used for inspection CFRPs owing to its excellent performance for bulk NDE inspections, the method is not sufficiently sensitive to all defect types occurring in such components. Ultrasonic waves transmitted using array probes on CFRP components mainly interact with defects that are extended perpendicularly to the direction of the wave propagation such as delamination. The technique does not offer sufficient sensitivity for the detection of shallow and narrow surface defects commonly created by matrix transversal cracking and barely visible impact damage mechanisms.<br/>The compound CFRP gives rise to the mixed electromagnetic properties where highly conductive carbon fibres are molded in a dielectric resin matrix. This provides a unique opportunity to explore the potential of electromagnetic NDE sensing modalities such as Eddy Currents (EC) and electrical Capacitance Imaging (CI) for inspection of surface defects. Accordingly, this feasibility study was aimed at investigating the design, automated robotic delivery, and performance assessment of different sensor technologies for the detection of surface defects through experiments. To this end, machined surface defects were fabricated in a CFRP sample. The automated robotic inspection was implemented for all UT, EC, and CI sensors individually where a novel sensor-enabled robotic system based on a real-time embedded controller was developed. The system components consisting of a KUKA robotic arm, Force/Torque (F/T) sensor, and NDE sensor and controller were interfaced through a core program in LabVIEW enabling a) real-time communication between different hardware, b) data acquisition from all sensors and c) full control of the processes within the cell. Moreover, real-time robot motion corrections driven by the F/T sensor feedback were established to adjust the contact force and orientation of the sensors to the component surface during the scan. All sensors, including the UT roller-probe, EC array, and CI sensor boards, were robotically delivered on the designated surface notches with varying depths of 0.1, 0.2, 0.5, and 5 mm. The results of EC and CI testing showed enhanced detectability with high SNR for the defects shallower than 0.2 mm when compared to the UT B-scan images.

Topics
  • impedance spectroscopy
  • surface
  • compound
  • polymer
  • Carbon
  • experiment
  • composite
  • defect
  • ultrasonic
  • resin
  • chemical ionisation