Fully convolutional network-based ultrasonic inversion for multi-layered bonded composites

Fully convolutional network-based ultrasonic inversion for multi-layered bonded composites

Mason Doust, Zhifei Xiao, Huadong Mo, Jing Rao

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Abstract. Ultrasonic methods are widely used for the detection and characterisation of defects in multi-layered bonded composites. However, quantitative reconstruction of defects, such as disbonds, which can affect adhesive bond integrity and severely reduce the strength of assemblies, remains challenging. In this work, a supervised full convolutional network (FCN)-based ultrasonic method is used to quantitatively reconstruct defects hidden in multi-layered bonded composites. This proposed method consists of a training process and a predicting process. In the training process, the FCN builds a non-linear mapping from the ultrasound data to the corresponding longitudinal (L-wave) velocity model. In the predicting process, the network obtained from the training process is used to directly reconstruct the L-wave velocity models from the new measured ultrasonic data of adhesively bonded composites. The simulation results show that the FCN-based ultrasonic inversion method has the ability to achieve the accurate quantitative reconstruction of ultrasonic L-wave velocity models of the high contrast defects, which has potential in online detection of multi-layered bonded composites.

Quantitative Reconstruction, Multi-Layered Bonded Composites, Deep Learning-Based Inversion, Defect Detection, Non-Destructive Evaluation

Published online 3/30/2023, 7 pages
Copyright © 2023 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA

Citation: Mason Doust, Zhifei Xiao, Huadong Mo, Jing Rao, Fully convolutional network-based ultrasonic inversion for multi-layered bonded composites, Materials Research Proceedings, Vol. 27, pp 315-321, 2023

DOI: https://doi.org/10.21741/9781644902455-41

The article was published as article 41 of the book Structural Health Monitoring

Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.

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