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

Dades bibliogràfiques
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 corporatiu:	Томский политехнический университет (570)
Altres autors:	Moskovchenko A. I. Aleksey Igorevich, Vavilov V. P. Vladimir Platonovich
Sumari:	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:	anglès
Publicat:	2024
Matèries:	Infrared thermography Nondestructive Testing Machine learning Numerical simulation Defect detection электронный ресурс труды учёных ТПУ
Accés en línia:	https://doi.org/10.3221/IGF-ESIS.70.10
Format:	Electrònic Capítol de llibre
KOHA link:	https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=675030

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