Automated Defect Detection in Non-planar Objects Using Deep Learning Algorithms

The non-uniformity of non-planar object inspection data makes their analysis challenging. This paper reports a study of the use of recurrent neural network and artificial feed-forward neural network in pulsed thermography during the automated inspection of non-planar carbon fiber reinforced plastic...

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Vydáno v:	Journal of nondestructive evaluation Ročník 41; číslo 1; s. 14
Hlavní autoři:	Tao, Yuntao, Hu, Caiqi, Zhang, Hai, Osman, Ahmad, Ibarra-Castanedo, Clemente, Fang, Qiang, Sfarra, Stefano, Dai, Xiaobiao, Maldague, Xavier, Duan, Yuxia
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York Springer US 01.03.2022 Springer Nature B.V
Témata:	Algorithms Artificial intelligence Automation Cameras Carbon fiber reinforced plastics Characterization and Evaluation of Materials Classical Mechanics Classification Control Cooling Data analysis Data processing Deep learning Defects Design Dynamical Systems Engineering Fourier transforms Inspection Machine learning Neural networks Nonuniformity Object recognition Radiation Recurrent neural networks Signal processing Signal reconstruction Solid Mechanics Thermography Time dependence Time series Vibration Long short-term memory recurrent neural network Carbon fiber reinforced plastic Artificial feed-forward neural networks Pulsed thermography Non-planar
ISSN:	0195-9298, 1573-4862
On-line přístup:	Získat plný text
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