Thermographic Data Processing and Feature Extraction Approaches for Machine Learning-Based Defect Detection

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23640%2F23%3A43971033" target="_blank" >RIV/49777513:23640/23:43971033 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.3390/engproc2023051005" target="_blank" >https://doi.org/10.3390/engproc2023051005</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/engproc2023051005" target="_blank" >10.3390/engproc2023051005</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Thermographic Data Processing and Feature Extraction Approaches for Machine Learning-Based Defect Detection

Popis výsledku v původním jazyce

Infrared thermography is a non-destructive testing method used to detect defects in materials and structures. Machine learning algorithms have been applied to thermographic data to automate the defect detection process. Data preparation and feature extraction are crucial factors affecting ML model results, especially in thermographic data analysis. This study focuses on automating the detection of impact damage in carbon fiber-reinforced polymer materials using flash-pulse thermography and ML algorithms. Various machine learning models and data pre-processing techniques were evaluated for their effectiveness in detecting and locating impact damage. The results demonstrated that the combination of the K-nearest neighbors model with the differential absolute contrast data processing method achieved the highest balanced accuracy. Other combinations, such as Gaussian support vector machine model with raw data and K-nearest neighbor with thermographic signal reconstruction derivative data, also exhibited promising performances.

Název v anglickém jazyce

Thermographic Data Processing and Feature Extraction Approaches for Machine Learning-Based Defect Detection

Popis výsledku anglicky

Infrared thermography is a non-destructive testing method used to detect defects in materials and structures. Machine learning algorithms have been applied to thermographic data to automate the defect detection process. Data preparation and feature extraction are crucial factors affecting ML model results, especially in thermographic data analysis. This study focuses on automating the detection of impact damage in carbon fiber-reinforced polymer materials using flash-pulse thermography and ML algorithms. Various machine learning models and data pre-processing techniques were evaluated for their effectiveness in detecting and locating impact damage. The results demonstrated that the combination of the K-nearest neighbors model with the differential absolute contrast data processing method achieved the highest balanced accuracy. Other combinations, such as Gaussian support vector machine model with raw data and K-nearest neighbor with thermographic signal reconstruction derivative data, also exhibited promising performances.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

20501 - Materials engineering

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název statě ve sborníku

Engineering Proceedings

ISBN

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ISSN

2673-4591

e-ISSN

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Počet stran výsledku

4

Strana od-do

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Název nakladatele

MDPI

Místo vydání

Basilej

Místo konání akce

Itálie

Datum konání akce

10. 9. 2023

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

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