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
| Parent link: | Fracture and Structural Integrity.— .— Cassino: Italian Group of Fracture Vol. 18, iss. 70.— 2024.— P. 177-191 |
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| Otros Autores: | , |
| Sumario: | 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 |
| Lenguaje: | inglés |
| Publicado: |
2024
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| Acceso en línea: | 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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| 200 | 1 | |a Enhancing Generalizability of a Machine Learning Model for Infrared Thermographic Defect Detection by Using 3D Numerical Modeling |f Arsenii Chulkov, Alexey Moskovchenko, Vladimir Vavilov | |
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| 330 | |a 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. | ||
| 336 | |a Текстовый файл | ||
| 461 | 1 | |t Fracture and Structural Integrity |n Italian Group of Fracture |c Cassino | |
| 463 | 1 | |t Vol. 18, iss. 70 |v P. 177-191 |d 2024 | |
| 610 | 1 | |a Infrared thermography | |
| 610 | 1 | |a Nondestructive Testing | |
| 610 | 1 | |a Machine learning | |
| 610 | 1 | |a Numerical simulation | |
| 610 | 1 | |a Defect detection | |
| 610 | 1 | |a электронный ресурс | |
| 610 | 1 | |a труды учёных ТПУ | |
| 700 | 1 | |a Chulkov |b A. O. |c specialist in the field of non-destructive testing |c Deputy Director for Scientific and Educational Activities; acting manager; Senior Researcher, Tomsk Polytechnic University, Candidate of Technical Sciences |f 1989- |g Arseniy Olegovich |9 16220 | |
| 701 | 1 | |a Moskovchenko |b A. I. |g Aleksey Igorevich | |
| 701 | 1 | |a Vavilov |b V. P. |c Specialist in the field of dosimetry and methodology of nondestructive testing (NDT) |c Doctor of technical sciences (DSc), Professor of Tomsk Polytechnic University (TPU) |f 1949- |g Vladimir Platonovich |9 16163 | |
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