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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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

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