| People | Locations | Statistics |
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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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Mohseni, Ehsan
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 detectioncitations
- 2023Application of eddy currents for inspection of carbon fibre composites
- 2023Application of machine learning techniques for defect detection, localisation, and sizing in ultrasonic testing of carbon fibre reinforced polymers
- 2023In-process non-destructive evaluation of metal additive manufactured components at build using ultrasound and eddy-current approachescitations
- 2023Mapping SEARCH capabilities to Spirit AeroSystems NDE and automation demand for composites
- 2023Using neural architecture search to discover a convolutional neural network to detect defects From volumetric ultrasonic testing data of composites
- 2023Phased array inspection of narrow-gap weld LOSWF defects for in-process weld inspection
- 2022Transfer learning for classification of experimental ultrasonic non-destructive testing images from synthetic data
- 2022Autonomous and targeted eddy current inspection from UT feature guided wave screening of resistance seam welds
- 2022Mechanical stress measurement using phased array ultrasonic system
- 2022Automated bounding box annotation for NDT ultrasound defect detection
- 2022Multi-sensor electromagnetic inspection feasibility for aerospace composites surface defects
- 2022Investigating ultrasound wave propagation through the coupling medium and non-flat surface of wire + arc additive manufactured components inspected by a PAUT roller-probe
- 2022Automated multi-modal in-process non-destructive evaluation of wire + arc additive manufacturing
- 2022Dual-tandem phased array inspection for imaging near-vertical defects in narrow gap welds
- 2022Targeted eddy current inspection based on ultrasonic feature guided wave screening of resistance seam welds
- 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
- 2022Automated real time eddy current array inspection of nuclear assetscitations
- 2020In-process calibration of a non-destructive testing system used for in-process inspection of multi-pass weldingcitations
- 2020Laser-assisted surface adaptive ultrasound (SAUL) inspection of samples with complex surface profiles using a phased array roller-probe
- 2019Ultrasonic phased array inspection of a Wire + Arc Additive Manufactured (WAAM) sample with intentionally embedded defectscitations
Places of action
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document
Mapping SEARCH capabilities to Spirit AeroSystems NDE and automation demand for composites
Abstract
Newly engineered and complex materials and processes such as composite and additive manufacturing are becoming an indispensable part of today's manufacturing economy owing to their potential to reduce material waste and carbon emissions whilst enhancing mechanical performance. To quantify and validate the high quality of manufacturing processes, and ensure safe in-service operation for these components, Non-Destructive Evaluation (NDE) sensor technologies, and their corresponding data acquisition and signal processing routines should evolve to better suit these new materials and processes. Besides, deployment of automated robotic systems has seen an increasing demand in the past decade as the repeatability, consistency, and speed of NDE scans offered through automation can boost the manufacturing throughput significantly.The large volumes of data generated through such automated NDE approaches require new intelligent algorithms for signal interpretation to sustain and match the pace of automated NDE.<br/>The Centre for Ultrasonic Engineering (CUE) has been supporting Spirit AeroSystems through a Royal Academy of Engineering Research Chair to drive the research and innovation in three distinct themes of a) sensor technology, b) automation and robotic sensor deployment, and c) data interpretation through machine learning. This presentation will provide an overview of different NDE challenges in manufacturing of composites at Spirit AeroSystems and discuss the approaches undertaken to tackle these by the team at CUE. This includes proposing a roadmap inspired by the current research efforts for future of NDE in aerospace composite manufacturing.