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Addendum published on 12 April 2021, see Appl. Sci. 2021, 11(8), 3451.
Article

A Method of Defect Depth Estimation for Simulated Infrared Thermography Data with Deep Learning

Computer Vision and Systems Laboratory, Department of Electrical and Computer Engineering, Université Laval, 1065, av. de la Médecine, Québec QC G1V 0A6, Canada
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Appl. Sci. 2020, 10(19), 6819; https://doi.org/10.3390/app10196819
Received: 25 August 2020 / Revised: 21 September 2020 / Accepted: 25 September 2020 / Published: 29 September 2020
(This article belongs to the Special Issue Structural Health Monitoring & Nondestructive Testing)
Infrared thermography has already been proven to be a significant method in non-destructive evaluation since it gives information with immediacy, rapidity, and low cost. However, the thorniest issue for the wider application of IRT is quantification. In this work, we proposed a specific depth quantifying technique by employing the Gated Recurrent Units (GRUs) in composite material samples via pulsed thermography (PT). Finite Element Method (FEM) modeling provides the economic examination of the response pulsed thermography. In this work, Carbon Fiber Reinforced Polymer (CFRP) specimens embedded with flat bottom holes are stimulated by a FEM modeling (COMSOL) with precisely controlled depth and geometrics of the defects. The GRU model automatically quantified the depth of defects presented in the stimulated CFRP material. The proposed method evaluated the accuracy and performance of synthetic CFRP data from FEM for defect depth predictions. View Full-Text
Keywords: NDT Methods; defects depth estimation; pulsed thermography; Gated Recurrent Units NDT Methods; defects depth estimation; pulsed thermography; Gated Recurrent Units
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MDPI and ACS Style

Fang, Q.; Maldague, X. A Method of Defect Depth Estimation for Simulated Infrared Thermography Data with Deep Learning. Appl. Sci. 2020, 10, 6819. https://doi.org/10.3390/app10196819

AMA Style

Fang Q, Maldague X. A Method of Defect Depth Estimation for Simulated Infrared Thermography Data with Deep Learning. Applied Sciences. 2020; 10(19):6819. https://doi.org/10.3390/app10196819

Chicago/Turabian Style

Fang, Qiang, and Xavier Maldague. 2020. "A Method of Defect Depth Estimation for Simulated Infrared Thermography Data with Deep Learning" Applied Sciences 10, no. 19: 6819. https://doi.org/10.3390/app10196819

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