Automated procedure for detecting and characterizing defects in GFRP composite by using thermal nondestructive testing; Infrared Physics and Technology; Vol. 114

Automated procedure for detecting and characterizing defects in GFRP composite by using thermal nondestructive testing; Infrared Physics and Technology; Vol. 114

Bibliographic Details
Parent link:	Infrared Physics and Technology Vol. 114.— 2021.— [103675, 9 p.]
Corporate Authors:	Национальный исследовательский Томский политехнический университет Инженерная школа неразрушающего контроля и безопасности Центр промышленной томографии Научно-производственная лаборатория "Бетатронная томография крупногабаритных объектов", Национальный исследовательский Томский политехнический университет Инженерная школа неразрушающего контроля и безопасности Центр промышленной томографии Научно-производственная лаборатория "Тепловой контроль"
Other Authors:	Chulkov A. O. Arseniy Olegovich, Nesteruk D. A. Denis Alekseevich, Vavilov V. P. Vladimir Platonovich, Shagdyrov B. I. Bator Ilyich, Omar M. Mohammed, Siddiqui A. O. Ahmad, Prasad Y. L. V. D.
Summary:	Title screen The paper describes the concept of an automated defect characterization procedure by using infrared nondestructive testing of glass fiber reinforced composite. The proposed algorithms have allowed determination of defect depth, lateral dimensions and area, as well as coordinates of defect centers. The algorithms are based on the use of the neural network trained on both experimental and theoretical temperature profiles. An acceptable for practice accuracy of defect characterization has been obtained on the experimental data (0–15% by defect depth and 26–139% by defect area). Режим доступа: по договору с организацией-держателем ресурса
Language:	English
Published:	2021
Subjects:	электронный ресурс труды учёных ТПУ active infrared thermography automated defect detection automated defect characterization data processing neural network glass fiber reinforced composite инфракрасная термография автоматическое обнаружение обработка данных дефекты нейронные сети композиты
Online Access:	https://doi.org/10.1016/j.infrared.2021.103675
Format:	Electronic Book Chapter
KOHA link:	https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=665698

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