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Title:

Quantitative reconstruction of defects in multi-layered bonded composites using fully convolutional network-based ultrasonic inversion

Document type:
Zeitschriftenaufsatz
Author(s):
Rao, Jing; Yang, Fangshu; Mo, Huadong; Kollmannsberger, Stefan; Rank, Ernst
Abstract:
Ultrasonic methods have great potential applications to detect and characterize defects in multi-layered bonded composites. However, it remains challenging to quantitatively reconstruct defects, such as disbonds and kissing bonds, that influence the integrity of adhesive bonds and seriously reduce the strength of assemblies. In this work, an ultrasonic method based on the supervised fully convolutional network (FCN) is proposed to quantitatively reconstruct defects hidden in multi-layered bonded...     »
Journal title:
Journal of Sound and Vibration
Year:
2022
Year / month:
2020-11
Quarter:
4. Quartal
Month:
Nov
Pages contribution:
117418
Covered by:
Scopus
Reviewed:
ja
Fulltext / DOI:
doi:doi.org/10.1016/j.jsv.2022.117418
WWW:
https://www.researchgate.net/publication/363651556_Immersed_boundary_parametrizations_for_full_waveform_inversion
Status:
Preprint / submitted
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