Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed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 (12:29:30 CEST)

A peer-reviewed article of this Preprint also exists.

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


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 wider application of IRT is the 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). 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 quantified the depth of defects presented in the CFRP material. The proposed method evaluated the accuracy and performance of synthetic CFRP data from FEM for defect depth predictions.


NDT Methods; Defects depth estimation; Pulsed thermography; Gated Recurrent Units


ENGINEERING, Automotive Engineering

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