Automated CFRP impact damage detection with statistical thermographic data and machine learning

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23640%2F25%3A43974591" target="_blank" >RIV/49777513:23640/25:43974591 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1016/j.ijthermalsci.2024.109411" target="_blank" >https://doi.org/10.1016/j.ijthermalsci.2024.109411</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.ijthermalsci.2024.109411" target="_blank" >10.1016/j.ijthermalsci.2024.109411</a>

Result language
angličtina
Original language name
Automated CFRP impact damage detection with statistical thermographic data and machine learning
Original language description
The study is focused on the use of machine learning models for the automated detection of impact damage in carbon fiber reinforced polymer (CFRP) by flash-pulse thermographic testing. A new method for thermographic data pre-processing, which is based on statistical features, was proposed. Nine machine learning models for the automated detection of impact damage in CFRP samples were applied to the raw thermographic data, data preprocessed by the suggested method and data pre-processed by the widely used thermographic signal reconstruction (TSR) method. The machine learning models were tested to provide a binary classification of impact damage in CFRP. The results presented in this study show improved performance of the classification if the data are pre-processed by the proposed method. The best results were obtained by a Bagged tree ensemble trained with statistical features. The final balanced accuracy achieved for the Bagged trees model trained on 40 statistical features was 99.8 % which indicates a very good performance.
Czech name
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Czech description
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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
20501 - Materials engineering

Project
<a href="/en/project/EF18_069%2F0010018" target="_blank" >EF18_069/0010018: LABIR-PAV / Pre-application research of infrared technologies</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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