Preprint Article Version 2 Preserved in Portico This version is not peer-reviewed

Defect Depth Estimation in Infrared Thermography with Deep Learning

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)

A peer-reviewed article of this Preprint also exists.

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.

Journal reference: Appl. Sci. 2020, 10, 6819
DOI: 10.3390/app10196819

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.

Keywords

NDT methods; defects depth estimation; deep learning; pulsed thermography; gated recurrent unites

Comments (1)

Comment 1
Received: 22 March 2021
Commenter: Qiang Fang
Commenter's Conflict of Interests: Author
Comment: The important content of the article updated
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