Enhancing Generalizability of a Machine Learning Model for Infrared Thermographic Defect Detection by Using 3D Numerical Modeling; Fracture and Structural Integrity; Vol. 18, iss. 70

Enhancing Generalizability of a Machine Learning Model for Infrared Thermographic Defect Detection by Using 3D Numerical Modeling; Fracture and Structural Integrity; Vol. 18, iss. 70

Detalles Bibliográficos
Parent link:Fracture and Structural Integrity.— .— Cassino: Italian Group of Fracture
Vol. 18, iss. 70.— 2024.— P. 177-191
Autor Principal: Chulkov A. O. Arseniy Olegovich
Autor Corporativo: National Research Tomsk Polytechnic University (570)
Outros autores: Moskovchenko A. I. Aleksey Igorevich, Vavilov V. P. Vladimir Platonovich
Summary:Title screen
The paper describes the implementation of 3D numerical simulation in machine learning models used in infrared thermographic nondestructive testing. The enhancement of generalizability of such models emerges as a decisive factor for producing trust-worthy test results. First, it is demonstrated that the models trained on datasets with fixed parameters yield limited defect detection capabilities. The concept of training datasets, which include subtle variations in material thickness, thermal conductivity, as well as various combinations of material density and heat capacity, provides the best learning results and a noticeable ability to identify defects in all test datasets. Second, the model robustness in respect to noise is explored to demonstrate its ability to withstand additive and multiplicative random noise. Third, potentials of some known techniques of thermographic data processing, such as Thermographic Signal Reconstruction, Fast Fourier Transform and Temperature Contrast, are examined. In particular, the use of the Temperature Contrast data ensured sensitivity (True Positive Rate) better than 98% across all test datasets.
Текстовый файл
Idioma:inglés
Publicado: 2024
Subjects:
Infrared thermography
Nondestructive Testing
Machine learning
Numerical simulation
Defect detection
электронный ресурс
труды учёных ТПУ
Acceso en liña:https://doi.org/10.3221/IGF-ESIS.70.10
Formato: Electrónico Capítulo de libro
KOHA link:https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=675030

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