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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
Multi-sensor electromagnetic inspection feasibility for aerospace composites surface defects
Abstract
UK's presence at the forefront of composite manufacturing in Europe has never been more important provided how vital these structures are for i) slowing the climate change through reduction of fuel consumption and carbon footprint in different industries, and ii) development of wind and tidal blades to generate cleaner energy to achieve the net-zero target by the middle of the century. Therefore, the composite technology, Carbon Fibre Reinforced Polymers (CFRP) in particular, has been dominating the aerospace, energy, and defense industries, and this trend is expected to grow in the years to come. Non-Destructive Evaluation (NDE) is essential during manufacturing: to identify any defects early in the process as, if defects remain undetected, they could have far-reaching implications for the cost of scraped/repaired parts and the safety of final components, and ii) at later stages of manufacturing and post-manufacturing: to ensure the quality, integrity, and fitness for service of these safetycritical components. Although Ultrasound Testing (UT) has been predominantly used for inspection CFRPs owing to its excellent performance for bulk NDE inspections, the method is not sufficiently sensitive to all defect types occurring in such components. Ultrasonic waves transmitted using array probes on CFRP components mainly interact with defects that are extended perpendicularly to the direction of the wave propagation such as delamination. The technique does not offer sufficient sensitivity for the detection of shallow and narrow surface defects commonly created by matrix transversal cracking and barely visible impact damage mechanisms.<br/>The compound CFRP gives rise to the mixed electromagnetic properties where highly conductive carbon fibres are molded in a dielectric resin matrix. This provides a unique opportunity to explore the potential of electromagnetic NDE sensing modalities such as Eddy Currents (EC) and electrical Capacitance Imaging (CI) for inspection of surface defects. Accordingly, this feasibility study was aimed at investigating the design, automated robotic delivery, and performance assessment of different sensor technologies for the detection of surface defects through experiments. To this end, machined surface defects were fabricated in a CFRP sample. The automated robotic inspection was implemented for all UT, EC, and CI sensors individually where a novel sensor-enabled robotic system based on a real-time embedded controller was developed. The system components consisting of a KUKA robotic arm, Force/Torque (F/T) sensor, and NDE sensor and controller were interfaced through a core program in LabVIEW enabling a) real-time communication between different hardware, b) data acquisition from all sensors and c) full control of the processes within the cell. Moreover, real-time robot motion corrections driven by the F/T sensor feedback were established to adjust the contact force and orientation of the sensors to the component surface during the scan. All sensors, including the UT roller-probe, EC array, and CI sensor boards, were robotically delivered on the designated surface notches with varying depths of 0.1, 0.2, 0.5, and 5 mm. The results of EC and CI testing showed enhanced detectability with high SNR for the defects shallower than 0.2 mm when compared to the UT B-scan images.