Version 1
: Received: 25 August 2020 / Approved: 26 August 2020 / Online: 26 August 2020 (08:40:27 CEST)
Version 2
: Received: 16 March 2021 / Approved: 22 March 2021 / Online: 22 March 2021 (16:04:13 CET)
Fang, Q.; Maldague, X. A Method of Defect Depth Estimation for Simulated Infrared Thermography Data with Deep Learning. Appl. Sci.2020, 10, 6819.
Fang, Q.; Maldague, X. A Method of Defect Depth Estimation for Simulated Infrared Thermography Data with Deep Learning. Appl. Sci. 2020, 10, 6819.
Fang, Q.; Maldague, X. A Method of Defect Depth Estimation for Simulated Infrared Thermography Data with Deep Learning. Appl. Sci.2020, 10, 6819.
Fang, Q.; Maldague, X. A Method of Defect Depth Estimation for Simulated Infrared Thermography Data with Deep Learning. Appl. Sci. 2020, 10, 6819.
Abstract
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 (GRU) 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.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Commenter: Qiang Fang
Commenter's Conflict of Interests: Author