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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PeopleLocationsStatistics
Naji, M.
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University of Strathclyde

in Cooperation with on an Cooperation-Score of 37%

Topics

Publications (22/22 displayed)

  • 20243-Dimensional residual neural architecture search for ultrasonic defect detection5citations
  • 2023Application of eddy currents for inspection of carbon fibre compositescitations
  • 2023Application of machine learning techniques for defect detection, localisation, and sizing in ultrasonic testing of carbon fibre reinforced polymers citations
  • 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
  • 2023Using neural architecture search to discover a convolutional neural network to detect defects From volumetric ultrasonic testing data of compositescitations
  • 2023Phased array inspection of narrow-gap weld LOSWF defects for in-process weld inspectioncitations
  • 2022Transfer learning for classification of experimental ultrasonic non-destructive testing images from synthetic datacitations
  • 2022Autonomous and targeted eddy current inspection from UT feature guided wave screening of resistance seam weldscitations
  • 2022Mechanical stress measurement using phased array ultrasonic systemcitations
  • 2022Automated bounding box annotation for NDT ultrasound defect detectioncitations
  • 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
  • 2022Dual-tandem phased array inspection for imaging near-vertical defects in narrow gap weldscitations
  • 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
  • 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
  • 2019Ultrasonic phased array inspection of a Wire + Arc Additive Manufactured (WAAM) sample with intentionally embedded defects74citations

Places of action

Chart of shared publication
Tunukovic, Vedran
6 / 6 shared
Mackinnon, Christopher
3 / 3 shared
Wathavana Vithanage, Randika Kosala
11 / 11 shared
Ohare, Tom
5 / 5 shared
Mcknight, Shaun
7 / 7 shared
Macleod, Charles N.
21 / 45 shared
Pierce, Stephen
19 / 51 shared
Munro, Gavin
1 / 1 shared
Burnham, Kenneth Charles
1 / 1 shared
Foster, Euan
5 / 8 shared
Dobie, Gordon
4 / 21 shared
Obrien-Oreilly, J.
3 / 3 shared
Pyle, Richard
2 / 2 shared
Munro, G.
3 / 3 shared
Ohare, T.
3 / 3 shared
Mcknight, S.
3 / 3 shared
Halavage, Steven
4 / 6 shared
Loukas, Charalampos
8 / 13 shared
Ding, Jialuo
6 / 39 shared
Williams, Stewart
6 / 39 shared
Rizwan, Muhammad Khalid
3 / 4 shared
Misael, Pimentel Espirindio E. Silva
4 / 5 shared
Mckegney, Scott
4 / 6 shared
Lines, David
12 / 18 shared
Foster, Euan A.
1 / 2 shared
Zimermann, Rastislav
7 / 9 shared
Fitzpatrick, Stephen
4 / 14 shared
Vasilev, Momchil
10 / 17 shared
Poole, A.
1 / 2 shared
Mcinnes, M.
2 / 2 shared
Hifi, A.
1 / 1 shared
Gomez, R.
1 / 3 shared
Shields, M.
1 / 1 shared
Nicolson, Ewan
3 / 5 shared
Tant, Katherine Margaret Mary
1 / 5 shared
Mcinnes, Martin
3 / 3 shared
Gachagan, Anthony
9 / 76 shared
Bernard, Robert
3 / 5 shared
Bolton, Gary
3 / 5 shared
Hutchison, Alistair
1 / 1 shared
Mehnen, Jorn
1 / 4 shared
Lotfian, Saeid
1 / 22 shared
Javadi, Yashar
5 / 31 shared
Lawley, Alistair
1 / 1 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
Macdonald, Charles
1 / 1 shared
Pierce, Stephen Gareth
1 / 3 shared
Foster, Euan Alexander
1 / 1 shared
Stratoudaki, Theodosia
1 / 7 shared
Mineo, Carmelo
2 / 15 shared
Qiu, Zhen
2 / 14 shared
Sweeney, Nina E.
1 / 3 shared
Su, Riliang
1 / 3 shared
Chart of publication period
2024
2023
2022
2020
2019

Co-Authors (by relevance)

  • Tunukovic, Vedran
  • Mackinnon, Christopher
  • Wathavana Vithanage, Randika Kosala
  • Ohare, Tom
  • Mcknight, Shaun
  • Macleod, Charles N.
  • Pierce, Stephen
  • Munro, Gavin
  • Burnham, Kenneth Charles
  • Foster, Euan
  • Dobie, Gordon
  • Obrien-Oreilly, J.
  • Pyle, Richard
  • Munro, G.
  • Ohare, T.
  • Mcknight, S.
  • 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
  • Poole, A.
  • Mcinnes, M.
  • Hifi, A.
  • Gomez, R.
  • Shields, M.
  • Nicolson, Ewan
  • Tant, Katherine Margaret Mary
  • Mcinnes, Martin
  • Gachagan, Anthony
  • Bernard, Robert
  • Bolton, Gary
  • Hutchison, Alistair
  • Mehnen, Jorn
  • Lotfian, Saeid
  • Javadi, Yashar
  • Lawley, Alistair
  • Foster, E.
  • Burnham, K.
  • Gover, H.
  • Paton, S.
  • Grosser, M.
  • Macdonald, Charles
  • Pierce, Stephen Gareth
  • Foster, Euan Alexander
  • Stratoudaki, Theodosia
  • Mineo, Carmelo
  • Qiu, Zhen
  • Sweeney, Nina E.
  • Su, Riliang
OrganizationsLocationPeople

conferencepaper

Targeted eddy current inspection based on ultrasonic feature guided wave screening of resistance seam welds

  • Lines, David
  • Loukas, Charalampos
  • Mohseni, Ehsan
  • Mcinnes, Martin
  • Gachagan, Anthony
  • Foster, Euan
  • Bernard, Robert
  • Vasilev, Momchil
  • Mcknight, Shaun
  • Bolton, Gary
  • Macleod, Charles N.
Abstract

Non-Destructive Testing (NDT) of manufactured components has traditionally been expensive and labour intensive. Such issues are compounded further in safety-conscious industries such as nuclear and aerospace. With the advent of industry 4.0, an opportunity to exploit the intersection of many different NDT modalities to increase the productivity of current inspection regimes presents itself via robotic control. Two of the most common inspection modalities are ultrasonic and eddy current testing, with many benefits being derived from leveraging their respective advantages. Within the broader family of ultrasonic NDT, guided wave inspection has mainly been used as a screening tool to test long lengths of components from a single transducer location. It has also been shown that Feature Guided Waves (FGWs) that have their energy confined to a topological feature within a component’s geometry exist. As a result, FGWs offer much promise when it comes to targeted screening of key structural features such as welds or adhesive bonds. Moreover, due to the inherent dispersion, guided wave testing has proven to be complex, making operator training paramount and increasing the cost of industrial deployment. Furthermore, it is common to use a localised NDT modality in combination with guided wave testing when attempting defect characterisation creating further cost and time demands on operators.<br/>To relieve these pain points and realise the benefits of using multiple inspection modalities, the authors present the use of a flexible robotic system to flag potential defective regions within resistance seam welded (RSW) components via a novel ultrasonic FGW technique, and then perform targeted raster scans using an eddy current array on any of the identified defective regions. RSWs are used to seal nuclear grade canisters and represent a key industrial area that could benefit from data sharing across NDT modalities. A novel FGW was studied in detail through simulations and experiments. A weld guided mode like that of the fundamental antisymmetric mode of a free plate was discovered to have high energy concentration in the RSW joint and could readily detect transversal defects of ≥1mm in depth.The FGW technique was deployed in a semi-autonomous fashion lowering the aforementioned technical deployment barriers. Control of the robotic system as well as the ultrasonic and eddy current data acquisition, was performed within the LabVIEW software environment. This common integration allowed for seamless sharing of key parameters between the FGW and eddy current inspection modalities.<br/>For simplicity, flat RSW plates with transversal EDM notches ranging from 1mm depth and above were manufactured where the EDM notches represented transversal cracks within the component. Several experiments were performed on these samples where the inspection time associated with targeted raster scanning of the eddy current array on defective regions was compared to that of untargeted raster scanning of the entire component. It was shown that combining such techniques within a robotic environment greatly increases the productivity and lowers the time taken to effectively scan for defects within key structural features by at least a factor of 5. Future work is now focusing on expanding the results observed for the flat plate RSW samples to cylindrical RSW samples representative of sealed nuclear canisters.

Topics
  • impedance spectroscopy
  • dispersion
  • experiment
  • simulation
  • crack
  • defect
  • ultrasonic