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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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Tunukovic, Vedran
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (6/6 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
- 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
- 2022Automated bounding box annotation for NDT ultrasound defect detection
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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.