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H. HALLOUA1,* , A. ELHASSNAOUI2, A. ZRHAIBA2, S. KRAIBAA1, A. OBBADI1, Y. ERRAMI1, S. SAHNOUN1
- Laboratory of Electronics, Instrumentation and Energetic, Faculty of Sciences, B.P 20. 24000 El Jadida, Morocco
- Industrial Engineering Laboratory, Faculty of Science and Technology, BP: 523 Beni Mellal, Morocco
The use of carbon fiber materials is continually increasing in various industrial sectors because of their excellent thermomechanical properties. This work presents the implementation of a new approach based on a neural network for the processing of pulsed thermography data, to determine the internal defects depths in CFRP material. Preprocessing of network training data, using standard thermal contrast and principal component analysis has reduced the number of neural network inputs. The elaborated neural network was tested on simulation data with deviations not exceeding 5%. Experimental validation confirmed the proposed method’s effectiveness for evaluating the internal defects depths in CFRP composites..
Non-destructive Testing, Infrared thermography, neural network, defect depth, carbon fiber reinforced polymers, CFRP.
Submitted at: Oct. 11, 2019
Accepted at: April 9, 2020
H. HALLOUA, A. ELHASSNAOUI, A. ZRHAIBA, S. KRAIBAA, A. OBBADI, Y. ERRAMI, S. SAHNOUN, Defects depth estimation in a CFRP material by active infrared thermography using neural network, Journal of Optoelectronics and Advanced Materials Vol. 22, Iss. 3-4, pp. 156-162 (2020)
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