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Defects depth estimation in a CFRP material by active infrared thermography using neural network

H. HALLOUA1,* , A. ELHASSNAOUI2, A. ZRHAIBA2, S. KRAIBAA1, A. OBBADI1, Y. ERRAMI1, S. SAHNOUN1

Affiliation

  1. Laboratory of Electronics, Instrumentation and Energetic, Faculty of Sciences, B.P 20. 24000 El Jadida, Morocco
  2. Industrial Engineering Laboratory, Faculty of Science and Technology, BP: 523 Beni Mellal, Morocco

Abstract

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..

Keywords

Non-destructive Testing, Infrared thermography, neural network, defect depth, carbon fiber reinforced polymers, CFRP.

Submitted at: Oct. 11, 2019
Accepted at: April 9, 2020

Citation

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)