Damage characterization of embedded defects in composites using a hybrid thermography, computational, and artificial neural networks approach
Author:
Funder
King Fahd University of Petroleum and Minerals
Publisher
Elsevier BV
Subject
Multidisciplinary
Reference16 articles.
1. Characterization of defects in curved composite structures using active infrared thermography;Pastuszak;Procedia Eng.,2016
2. Damage characterization using thermography of composite plates subjected to low velocity impact loads;Al-Athel;J. Mater. Sci. Eng.,2019
3. Characterizing damage in CFRP structures using flash thermography in reflection and transmission configurations;Maierhofer;Compos. B Eng.,2014
4. Depth determination of defects in CFRP-structures using lock-in thermography;Ekanayake;Compos. B Eng.,2018
5. Delamination detection in glass-epoxy composites using step-phase thermography (SPT);Ghadermazi;Infrared Phys. Technol.,2015
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1. On the Use of Machine Learning for Damage Assessment in Composite Structures: A Review;Applied Composite Materials;2023-09-19
2. Damage Detection in Steel Beams Using Generalized Flexibility Quotient Difference Based Damage Index and Artificial Neural Network;Journal of Vibration Engineering & Technologies;2023-05-24
3. Lock-In Thermal Test Simulation, Influence, and Optimum Cycle Period for Infrared Thermal Testing in Non-Destructive Testing;Sensors;2022-12-28
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