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

In-process non-destructive evaluation of wire + arc additive manufacture components using ultrasound high-temperature dry-coupled roller-probe

  • Halavage, Steven
  • Loukas, Charalampos
  • Mohseni, Ehsan
  • Ding, Jialuo
  • Williams, Stewart
  • Macleod, Charles N.
  • Misael, Pimentel Espirindio E. Silva
  • Mckegney, Scott
  • Lines, David
  • Wathavana Vithanage, Randika Kosala
  • Zimermann, Rastislav
  • Fitzpatrick, Stephen
  • Vasilev, Momchil
  • Pierce, Stephen
Abstract

In 2019, the global metal Additive Manufacturing (AM) market size was valued at € 2.02 billion and was predicted to grow by up to 27.9% annually until 2024. Additive Manufacturing plays a significant role in Industry 4.0, where the demand for smart factories capable of fabricating high-quality customized products cost-efficiently exists. Wire + Arc Additive Manufacturing (WAAM) is one such technique that WAAM utilizes industrial robotics and arc-based welding processes to produce components on a layer-by-layer basis. is enables automated, time and material-efficient production of high-value and geometrically complex metal parts. To strengthen the benefits, the demand for robotically deployed in-process Non-Destructive Evaluation (NDE) has risen, aiming to replace manually deployed inspection techniques deployed after the full part completion. <br/>The research presents a new synchronized multi-robot WAAM deposition &amp; ultrasound NDE cell aiming to achieve defect detection in-process, enable possible in-process repair, and prevent costly scrappage or rework. Within the cell, the plasma-arc WAAM process, controlled by deposition software, is employed to build components. The full external control NDE approach is achieved by the real-time force/torque sensor-enabled adaptive kinematics control package. A high-temperature dry-coupled ultrasound roller-probe device is employed to assess the structural integrity of freshly deposited layers of WAAM components. The WAAM roller-probe is tailored to facilitate the in-process inspection by dry-coupling coupling with the hot (&lt; 350 °C) non-flat surface of WAAM using a flexible outer silicone tyre and solid core delay-line at speed and at coupling high force[1-3].<br/>The demonstration of the in-process inspection approach is performed on hot as-built titanium (Ti-6Al-4V) WAAM samples. The defect detection capabilities are assessed on artificial tungsten reflectors embedded in WAAM builds. In this work the defect detection is accomplished and analyzed using two separate approaches 1) layer-specific beamforming focusing imaging and 2) volumetric inspection using post-processing algorithms applied on collected Full Matric Capture data. The ultrasound in-process inspection using the dry-coupled roller-probe is driven by live Ultrasound Testing (UT) data acquisition, initiated within a minute from layer deposition completion. The collected UT B-scan frames are based on electronically focused beamforming through the roller-probe media into the depth of targeted layers.Subsequently, the results are presented on a plotted C-scan image, showing a top view over the interior of the targeted built volume. The results in this work are analyzed and compared to the X-ray computed tomography scan, conducted after the full-built completion and sample processing. The processed UT images show positionally accurate detection of embedded tungsten reflectors, with a minimum of 15 dB of signal-to-noise ratio. An accurate size estimation is also achieved for the tungsten defect extended along the sample’s length. <br/>The outcome of this research shows a successful defect detection and hence directly supports the industrial benefits of the WAAM process intending to achieve the automated production of first-time-right parts.<br/>

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