Issue 70

A. Chulkov et alii, Frattura ed Integrità Strutturale, 70 (2024) 177-191; DOI: 10.3221/IGF-ESIS.70.10

Enhancing generalizability of a machine learning model for infrared thermographic defect detection by using 3d numerical modeling

Arsenii Chulkov Tomsk Polytechnic University, Russia chulkovao@tpu.ru, http://orcid.org/0000-0003-1226-0013 Alexey Moskovchenko University of West Bohemia, Czech Republic alexeym@ntc.zcu.cz, http://orcid.org/0000-0002-2813-2529 Vladimir Vavilov Tomsk Polytechnic University, Russia vavilov@tpu.ru, http://orcid.org/ 0000-0002-9828-7374

Citation: Chulkov, A., Moskovchenko, A., Vavilov, V., Enhancing generalizability of a machine learning model for infrared thermographic defect detection by using 3D numerical modeling, Frattura ed Integrità Strutturale, 70 (2024) 177-191.

Received: 28.05.2024 Accepted: 26.07.2024 Published: 19.08.2024 Issue: 10.2024

Copyright: © 2024 This is an open access article under the terms of the CC-BY 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

K EYWORDS . Infrared thermography, Nondestructive Testing, Machine learning, Numerical simulation, Defect detection.

I NTRODUCTION nfrared (IR) thermography is a method of non-destructive testing (NDT) based on the analysis of thermal patterns on the surface of objects under test by using thermal imagers [1]. Thermal stimulation of objects and subsequent analysis of temperature distributions allow detecting structural defects and thermal anomalies in various materials. Due to its simplicity, non-contact nature of testing and capacity to swiftly assess large areas, IR thermographic NDT has become I

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