Enhancing generalizability of a machine learning model for infrared thermographic defect detection by using 3d numerical modeling
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23640%2F24%3A43972967" target="_blank" >RIV/49777513:23640/24:43972967 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.3221/IGF-ESIS.70.10" target="_blank" >https://doi.org/10.3221/IGF-ESIS.70.10</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.3221/IGF-ESIS.70.10" target="_blank" >10.3221/IGF-ESIS.70.10</a>
Alternative languages
Result language
angličtina
Original language name
Enhancing generalizability of a machine learning model for infrared thermographic defect detection by using 3d numerical modeling
Original language description
he 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
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20301 - Mechanical engineering
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Frattura ed Integrita Strutturale
ISSN
1971-8993
e-ISSN
1971-8993
Volume of the periodical
18
Issue of the periodical within the volume
70
Country of publishing house
IT - ITALY
Number of pages
15
Pages from-to
177-191
UT code for WoS article
001310355000001
EID of the result in the Scopus database
2-s2.0-85205589117