Dynamic infrared scanning thermography based on CNN: a novel large-scale honeycomb defect detection and classification technique; Journal of Thermal Analysis and Calorimetry; Vol. 150, iss. 11

Dynamic infrared scanning thermography based on CNN: a novel large-scale honeycomb defect detection and classification technique; Journal of Thermal Analysis and Calorimetry; Vol. 150, iss. 11

Detalles Bibliográficos
Parent link:Journal of Thermal Analysis and Calorimetry.— .— New York: Springer Nature
Vol. 150, iss. 11.— 2025.— P. 8189-8205
Autor Corporativo: Томский политехнический университет (570)
Otros Autores: Rui Li, Chiwu Bu, Hongpeng Zhang, Fei Wang, Vesala G. T. Gopi Tilak, Ghali Venkata Subbarao, Vavilov V. P. Vladimir Platonovich
Sumario:Title screen
This paper introduces a highly efficient technique, namely, dynamic infrared scanning thermography (DIST), for detecting defects in large-sized carbon fiber-reinforced polymer/aluminum (CFRP/Al) honeycomb composites. The corresponding test specimen with a high aspect ratio was fabricated for experimental validation by using a DIST system. The pseudo-static matrix reconstruction (PSMR) method and static image sequence processing algorithms were, respectively, employed to pre-process and post-process the experimental data. The results indicate that the DIST method can continuously and effectively detect defects in large-sized CFRP/Al specimens. The respective infrared image dataset was produced, and different convolutional neural network (CNN) models and optimizers were combined for training and comparatively performing automatic defect classification. The obtained results indicate that the combination of the SqueezeNet approach and stochastic gradient descent with momentum (SGDM) is the best when considering the training time as a figure of merit. Such combination provided the accuracy of 99.86% with the time cost of 8.6 min. Neglecting time costs, the combination of DarkNet19 and SGDM has proven to be the best ensuring the accuracy of 99.97%.
Текстовый файл
Lenguaje:inglés
Publicado: 2025
Materias:
Large-sized CFRP/Al honeycomb composites
Dynamic infrared thermal wave scanning NDT
Pseudo-static matrix reconstruction
CNN
Defect automatic classifcation
электронный ресурс
труды учёных ТПУ
Acceso en línea:https://doi.org/10.1007/s10973-024-13365-4
Formato: Electrónico Capítulo de libro
KOHA link:https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=675800

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