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 (11/11 displayed)

  • 20243-Dimensional residual neural architecture search for ultrasonic defect detection5citations
  • 2023In-process non-destructive evaluation of metal additive manufactured components at build using ultrasound and eddy-current approaches11citations
  • 2023Mapping SEARCH capabilities to Spirit AeroSystems NDE and automation demand for compositescitations
  • 2022Mechanical stress measurement using phased array ultrasonic systemcitations
  • 2022Multi-sensor electromagnetic inspection feasibility for aerospace composites surface defectscitations
  • 2022Investigating ultrasound wave propagation through the coupling medium and non-flat surface of wire + arc additive manufactured components inspected by a PAUT roller-probecitations
  • 2022Automated multi-modal in-process non-destructive evaluation of wire + arc additive manufacturingcitations
  • 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
  • 2020In-process calibration of a non-destructive testing system used for in-process inspection of multi-pass welding29citations
  • 2020Laser-assisted surface adaptive ultrasound (SAUL) inspection of samples with complex surface profiles using a phased array roller-probecitations

Places of action

Chart of shared publication
Tunukovic, Vedran
2 / 6 shared
Mackinnon, Christopher
1 / 3 shared
Ohare, Tom
1 / 5 shared
Mohseni, Ehsan
11 / 22 shared
Mcknight, Shaun
1 / 7 shared
Macleod, Charles N.
10 / 45 shared
Pierce, Stephen
10 / 51 shared
Halavage, Steven
4 / 6 shared
Loukas, Charalampos
5 / 13 shared
Ding, Jialuo
5 / 39 shared
Williams, Stewart
5 / 39 shared
Rizwan, Muhammad Khalid
3 / 4 shared
Misael, Pimentel Espirindio E. Silva
4 / 5 shared
Mckegney, Scott
4 / 6 shared
Lines, David
7 / 18 shared
Foster, Euan A.
1 / 2 shared
Zimermann, Rastislav
7 / 9 shared
Fitzpatrick, Stephen
4 / 14 shared
Vasilev, Momchil
6 / 17 shared
Dobie, Gordon
2 / 21 shared
Poole, A.
1 / 2 shared
Obrien-Oreilly, J.
2 / 3 shared
Pyle, Richard
1 / 2 shared
Munro, G.
2 / 3 shared
Ohare, T.
2 / 3 shared
Mcinnes, M.
2 / 2 shared
Hifi, A.
1 / 1 shared
Mcknight, S.
2 / 3 shared
Gomez, R.
1 / 3 shared
Shields, M.
1 / 1 shared
Hutchison, Alistair
1 / 1 shared
Mehnen, Jorn
1 / 4 shared
Lotfian, Saeid
1 / 22 shared
Gachagan, Anthony
5 / 76 shared
Javadi, Yashar
4 / 31 shared
Foster, E.
1 / 2 shared
Burnham, K.
1 / 1 shared
Gover, H.
1 / 1 shared
Paton, S.
1 / 1 shared
Grosser, M.
1 / 2 shared
Foster, Euan
2 / 8 shared
Macdonald, Charles
1 / 1 shared
Pierce, Stephen Gareth
1 / 3 shared
Stratoudaki, Theodosia
1 / 7 shared
Mineo, Carmelo
1 / 15 shared
Qiu, Zhen
2 / 14 shared
Sweeney, Nina E.
1 / 3 shared
Chart of publication period
2024
2023
2022
2020

Co-Authors (by relevance)

  • Tunukovic, Vedran
  • Mackinnon, Christopher
  • Ohare, Tom
  • Mohseni, Ehsan
  • Mcknight, Shaun
  • Macleod, Charles N.
  • Pierce, Stephen
  • Halavage, Steven
  • Loukas, Charalampos
  • Ding, Jialuo
  • Williams, Stewart
  • Rizwan, Muhammad Khalid
  • Misael, Pimentel Espirindio E. Silva
  • Mckegney, Scott
  • Lines, David
  • Foster, Euan A.
  • Zimermann, Rastislav
  • Fitzpatrick, Stephen
  • Vasilev, Momchil
  • Dobie, Gordon
  • Poole, A.
  • Obrien-Oreilly, J.
  • Pyle, Richard
  • Munro, G.
  • Ohare, T.
  • Mcinnes, M.
  • Hifi, A.
  • Mcknight, S.
  • Gomez, R.
  • Shields, M.
  • Hutchison, Alistair
  • Mehnen, Jorn
  • Lotfian, Saeid
  • Gachagan, Anthony
  • Javadi, Yashar
  • Foster, E.
  • Burnham, K.
  • Gover, H.
  • Paton, S.
  • Grosser, M.
  • Foster, Euan
  • Macdonald, Charles
  • Pierce, Stephen Gareth
  • Stratoudaki, Theodosia
  • Mineo, Carmelo
  • Qiu, Zhen
  • Sweeney, Nina E.
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