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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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

Název v anglickém jazyce

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

Popis výsledku anglicky

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

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20301 - Mechanical engineering

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Frattura ed Integrita Strutturale

ISSN

1971-8993

e-ISSN

1971-8993

Svazek periodika

18

Číslo periodika v rámci svazku

70

Stát vydavatele periodika

IT - Italská republika

Počet stran výsledku

15

Strana od-do

177-191

Kód UT WoS článku

001310355000001

EID výsledku v databázi Scopus

2-s2.0-85205589117