Autonomous damage recognition in visual inspection of laminated composite structures using deep learning

Fotouhi, S., Pashmforoush, F., Bodaghi, M. ORCID: 0000-0002-0707-944X and Fotouhi, M., 2021. Autonomous damage recognition in visual inspection of laminated composite structures using deep learning. Composite Structures, 268: 113960. ISSN 0263-8223

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This study proposes the exploitation of deep learning for quantitative assessment of visual detectability of different types of in-service damage in laminated composite structures such as aircraft and wind turbine blades. A comprehensive image-based data set is collected from the literature containing common microscale damage mechanisms (matrix cracking and fibre breakage) and macroscale damage mechanisms (impact and erosion). Then, automated classification of the damage type and severity was done by pre-trained version of AlexNet that is a stable convolutional neural network for image processing. Pre-trained ResNet-50 and 5 other user-defined convolutional neural networks were also used to evaluate the performance of AlexNet. The results demonstrated that employing AlexNet network, using the relatively small image dataset, provided the highest accuracy level (87%–96%) for identifying the damage severity and types in a reasonable computational time. The generated knowledge and the collected image data in this paper will facilitate further research and development in the field of autonomous visual inspection of composite structures with the potential to significantly reduce the costs, health and safety risks and downtime associated with integrity assessment.

Item Type: Journal article
Publication Title: Composite Structures
Creators: Fotouhi, S., Pashmforoush, F., Bodaghi, M. and Fotouhi, M.
Publisher: Elsevier
Date: 15 July 2021
Volume: 268
ISSN: 0263-8223
S0263822321004207Publisher Item Identifier
Rights: Attribution 4.0 International (CC BY 4.0)
Divisions: Schools > School of Science and Technology
Record created by: Jonathan Gallacher
Date Added: 28 Apr 2021 14:45
Last Modified: 13 Apr 2022 03:00

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