Corpus ID: 237513615

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

  title={Quantitative reconstruction of defects in multi-layered bonded composites using fully convolutional network-based ultrasonic inversion},
  author={Jing Rao and Fangshu Yang and Huadong Mo and Stefan Kollmannsberger and Ernst Rank},
  • Jing Rao, Fangshu Yang, +2 authors E. Rank
  • Published 11 September 2021
  • Computer Science, Physics
  • ArXiv
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… Expand

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