Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed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)

How to cite: Fang, Q.; Maldague, X. Defect Depth Estimation in Infrared Thermography with Deep Learning. Preprints 2020, 2020080565 (doi: 10.20944/preprints202008.0565.v1). Fang, Q.; Maldague, X. Defect Depth Estimation in Infrared Thermography with Deep Learning. Preprints 2020, 2020080565 (doi: 10.20944/preprints202008.0565.v1).

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.

Subject Areas

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

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