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| Naji, M. |
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| Motta, Antonella |
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| Aletan, Dirar |
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| Mohamed, Tarek |
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| Ertürk, Emre |
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| Taccardi, Nicola |
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| Kononenko, Denys |
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| Petrov, R. H. | Madrid |
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| Alshaaer, Mazen | Brussels |
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| Bih, L. |
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| Casati, R. |
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| Muller, Hermance |
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| Kočí, Jan | Prague |
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| Šuljagić, Marija |
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| Kalteremidou, Kalliopi-Artemi | Brussels |
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| Azam, Siraj |
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| Ospanova, Alyiya |
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| Blanpain, Bart |
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| Ali, M. A. |
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| Popa, V. |
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| Rančić, M. |
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| Ollier, Nadège |
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| Azevedo, Nuno Monteiro |
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| Landes, Michael |
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| Rignanese, Gian-Marco |
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Fitzpatrick, Stephen
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (14/14 displayed)
- 2023In-process non-destructive evaluation of metal additive manufactured components at build using ultrasound and eddy-current approachescitations
- 2023In-process non-destructive evaluation of metal additive manufactured components at build using ultrasound and eddy-current approachescitations
- 2022Collaborative robotic wire + arc additive manufacture and sensor-enabled in-process ultrasonic non-destructive evaluationcitations
- 2022Automated multi-modal in-process non-destructive evaluation of wire + arc additive manufacturing
- 2022In-process non-destructive evaluation of wire + arc additive manufacture components using ultrasound high-temperature dry-coupled roller-probe
- 2022Collaborative robotic Wire + Arc Additive Manufacture and sensor-enabled in-process ultrasonic Non-Destructive Evaluationcitations
- 2019Remanufacture of hot forging tools and dies using laser metal deposition with powder and a hard-facing alloy Stellite 21®citations
- 2018Remanufacture of hot forging tools and dies using Laser Metal Deposition with powder and a hard-facing alloy Stellite 21®citations
- 2018Remanufacture of hot forging tools and dies using Laser Metal Deposition with powder and a hard-facing alloy Stellite 21®citations
- 2017A full factorial numerical investigation and validation of precision end milling process for hardened tool steel
- 2016Investigating relationships between laser metal deposition deployment conditions and material microstructural evolution
- 2016Remanufacturing H13 steel moulds and dies using laser metal deposition
- 2016Wear behaviour of laser cladded Ni-based WC composite coating for Inconel hot extrusion
- 2012Correcting for a Density Distribution: Particle Size Analysis of Core-Shell Nanocomposite Particles Using Disk Centrifuge Photosedimentometrycitations
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article
Collaborative robotic Wire + Arc Additive Manufacture and sensor-enabled in-process ultrasonic Non-Destructive Evaluation
Abstract
<p>The demand for cost-efficient manufacturing of complex metal components has driven research for metal Additive Manufacturing (AM) such as Wire + Arc Additive Manufacturing (WAAM). WAAM enables automated, time-and material-efficient manufacturing of metal parts. To strengthen these benefits, the demand for robotically deployed in-process Non-Destructive Evaluation (NDE) has risen, aiming to replace current manually deployed inspection techniques after completion of the part. This work presents a synchronized multi-robot WAAM and NDE cell aiming to achieve (1) defect detection in-process, (2) enable possible in-process repair and (3) prevent costly scrappage or rework of completed defective builds. The deployment of the NDE during a deposition process is achieved through real-time position control of robots based on sensor input. A novel high-temperature capable, dry-coupled phased array ultrasound transducer (PAUT) roller-probe device is used for the NDE inspection. The dry-coupled sensor is tailored for coupling with an as-built high-temperature WAAM surface at an applied force and speed. The demonstration of the novel ultrasound in-process defect detection approach, presented in this paper, was performed on a titanium WAAM straight sample containing an intentionally embedded tungsten tube reflectors with an internal diameter of 1.0 mm. The ultrasound data were acquired after a pre-specified layer, in-process, employing the Full Matrix Capture (FMC) technique for subsequent post-processing using the adaptive Total Focusing Method (TFM) imaging algorithm assisted by a surface reconstruction algorithm based on the Synthetic Aperture Focusing Technique (SAFT). The presented results show a sufficient signal-to-noise ratio. Therefore, a potential for early defect detection is achieved, directly strengthening the benefits of the AM process by enabling a possible in-process repair.</p>