Layer-by-layer reconstruction of fatigue damages in composites from thermal images by a Residual U-Net-Reference-Cited by-同舟云学术

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Layer-by-layer reconstruction of fatigue damages in composites from thermal images by a Residual U-Net

Published:2024-08 Issue: Volume:255 Page:110712
ISSN:0266-3538
Container-title:Composites Science and Technology
language:en
Short-container-title:Composites Science and Technology

Author:

von Houwald Benedict,Sarhadi Ali,Eitzinger Christian,Eder Martin A.^ORCID

Funder

EUDP

Publisher

Elsevier BV

Reference31 articles.

1. A human-cyber-physical system toward intelligent wind turbine operation and maintenance;Chen;Sustainability (Switzerland),2021

2. AQUADA: Automated quantification of damages in composite wind turbine blades for LCOE reduction;Chen;Wind Energy,2021

3. Machine learning based thermal imaging damage detection in glass-epoxy composite materials;Sarhadi;Compos. Struct.,2022

4. Sensitivity and robustness of neural networks for defect-depth estimation in CFRP composites;Saeed;J. Nondestruct. Eval.,2019

5. Deep-learning based damage sensing of carbon fiber/polypropylene composite via addressable conducting network;Yu;Compos. Struct.,2021

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