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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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

document

Automated multi-modal in-process non-destructive evaluation of wire + arc additive manufacturing

  • Macdonald, Charles
  • Halavage, Steven
  • Loukas, Charalampos
  • Mohseni, Ehsan
  • Ding, Jialuo
  • Foster, Euan
  • Williams, Stewart
  • Rizwan, Muhammad Khalid
  • Misael, Pimentel Espirindio E. Silva
  • Mckegney, Scott
  • Lines, David
  • Wathavana Vithanage, Randika Kosala
  • Zimermann, Rastislav
  • Gachagan, Anthony
  • Fitzpatrick, Stephen
  • Vasilev, Momchil
  • Pierce, Stephen
Abstract

The scale of the global market size for metal Additive Manufacturing (AM) in recent years, at €2.02 billion in 2019, and predictions for the continuous growth up to 27.9% annually until 2024 highlight the key role that these processes will play in the future of high-value manufacturing. AM technology leverages the concepts of the latest industrial revolution, Industry 4.0, where the manufacturing is made more flexible and smarter capable of fabricating cost-effective high-quality customized products. Among the various AM technologies, only a few such as the Wire + Arc Additive Manufacturing (WAAM) process can meet some industries’ demands for producing large components in a short time. The process typically involves industrial robots and arc-based welding performing layer-by-layer deposition on substrates and building up components to their final desired shape. The process is majorly used to manufacture low volume, high mix, critical components for applications in aerospace and nuclear industries. Therefore, the imposed inspection requirements demand very high detection sensitivities. Robotically-deployed Non-Destructive Evaluation (NDE) during the manufacturing process might just be the inspection process needed to ensure the component’s integrity as it is being built, paving the way for an easier part certification process which is normally of concern to many end-users of the technology. In-process automated inspection of WAAM, deployed after deposition of every few layers, adds to the process cost-effectiveness as the early defect detection capability provides the opportunity for the process intervention and taking remedial rework actions reducing the time/material waste.<br/>This work presents a demonstration of the concept of in-process NDE of WAAM using two different modalities: a) a high temperature phased array Ultrasound Testing (UT) roller-probe, and b) a high-temperature flexible Eddy Currents (EC) testing array. The automation cell is composed of two robots, where one is dedicated to the WAAM deposition process and the other to NDE sensor delivery on the WAAM. A titanium WAAM component with a straight geometry was deposited using the plasma-arc process and oscillation strategy, where the deposition path and process parameters were controlled by software. Intentionally-embedded tungsten tube and ball reflectors of varying sizes/orientations were inserted in between different WAAM layers to assess the in-process detectability of each of the employed NDE modalities. Full external control of the sensor-enabled adaptive motion control for the NDE robot and the integrated UT and EC array controllers and array probes were achieved through a central program developed in the LabVIEW platform. Moreover, real-time robot motion corrections, driven by the Force-Torque sensor feedback, were established to adjust the contact force and orientation of the sensors to the component surface during the scan. The high-temperature (up to 350 °C), dry-coupled UT roller-probe inspection of WAAM was conducted in-process at the dwelling time between the layers while the surface was at a maximum temp of 130C. Subsequently, EC scan was also carried out in the dwell time at high temperature. The C-scans were produced live from both UT and EC arrays demonstrating the successful detection of embedded tungsten defects with high SNRs. The WAAM component was X-ray CT scanned after production to confirm the exact location if the defects and compare it against the other NDE findings.

Topics
  • Deposition
  • impedance spectroscopy
  • surface
  • defect
  • titanium
  • tungsten
  • wire
  • additive manufacturing