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 proved to be a significant method in non-destructive evaluation since it gives information with immediacy, rapidity and low cost. However, the thorniest issue for wider application of IRT is the quantification. In this work, we proposed a specific depth quantifying technique by employing the Gated Recurrent Unites (GRU) in composite material samples via pulsed thermography (PT). Carbon Fiber Reinforced Polymer (CFRP) embedded with flat bottom holes were designed via Finite Element Method (FEM) modeling in order to precisely control the depth and geometrics of the defects. The GRU model automatically quantify the depth of defects presented in the Plexiglasses materials. The proposed evaluated the accuracy and performance of synthetic plexiglasses data from FEM for defect depth predictions.
Engineering, Electrical and Electronic Engineering
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.
