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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
Automated bounding box annotation for NDT ultrasound defect detection
Abstract
The growing interest in applying Machine Learning (ML) techniques in Non-Destructive Testing (NDT) to assist expert detection and analysis is facing many unique challenges. This research seeks to create an object detection network that would automatically generate bounding boxes around various defects found in Carbon Fibre Reinforced Polymers (CFRPs) through which the quantitative defect size information can be inferred. CFRPs are structurally anisotropic resulting in complex physical interactions between the emitted acoustic waves and the material structure when Ultrasonic Testing (UT) is deployed. Therefore, the structural noise makes the detection of various types of defects, such as porosities, delaminations and inclusions, that are frequently observed in CFRPs [1] even a more challenging task. In order to take a supervised learning approach in the detection of defects, a training dataset must be produced and labelled. Extensive automatic methods for data collection exist, however, in many cases labelling is done manually, which requires extensive use of expert time. Therefore, a method for automatically labelling simple defects could potentially be useful for accelerating the ground truth creation and allowing experts to focus on the detection of more complex defects.