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Titel:

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

Dokumenttyp:
Zeitschriftenaufsatz
Autor(en):
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...     »
Zeitschriftentitel:
Journal of Sound and Vibration
Jahr:
2022
Jahr / Monat:
2020-11
Quartal:
4. Quartal
Monat:
Nov
Seitenangaben Beitrag:
117418
Nachgewiesen in:
Scopus
Reviewed:
ja
Volltext / 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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