Deep-learning based damage sensing of carbon fiber/polypropylene composite via addressable conducting network-Reference-Cited by-同舟云学术

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Deep-learning based damage sensing of carbon fiber/polypropylene composite via addressable conducting network

Published:2021-07 Issue: Volume:267 Page:113871
ISSN:0263-8223
Container-title:Composite Structures
language:en
Short-container-title:Composite Structures

Author:

Yu Myeong-Hyeon,Kim Hak-Sung

Funder

Ministry of Trade, Industry and Energy

Hanyang University

Korea Institute of Energy Technology Evaluation and Planning

Samsung

Publisher

Elsevier BV

Subject

Civil and Structural Engineering,Ceramics and Composites

Reference27 articles.

1. Damage sensing of adhesively-bonded hybrid composite/steel joints using carbon nanotubes;Lim;Compos Sci Technol,2011

2. Microstructural damage dependent stiffness prediction of unidirectional CFRP composite under cyclic loading;Senthilnathan;Compos A Appl Sci Manuf,2017

3. Interfacially reinforced unsaturated polyester carbon fiber composites with a vinyl ester-carbon nanotubes sizing agent;Wu;Compos Sci Technol,2018

4. Analysis of the damage mechanisms in mixed-mode delamination of laminated composites using acoustic emission data clustering;Fotouhi;J Thermoplast Compos Mater,2017

5. Enhancing fatigue resistance and damage characterisation in adhesively-bonded composite joints by carbon nanofibres;Ladani;Compos Sci Technol,2017

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2. Layer-by-layer reconstruction of fatigue damages in composites from thermal images by a Residual U-Net;Composites Science and Technology;2024-08

3. Integration of carbon nanotube yarns into glass‐fiber reinforced composites for electrical self‐sensing of damage under cyclic bending and impact loading;Polymer Composites;2024-07-26

4. Adhesion Testing System Based on Convolutional Neural Networks for Quality Inspection of Flexible Strain Sensors;IEEE Transactions on Industrial Informatics;2024-07

5. A novel version of hierarchical genetic algorithm and its application for hyperparameters optimization in CNN models for structural delamination identification;Journal of the Brazilian Society of Mechanical Sciences and Engineering;2024-06-20

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