Automated procedure for detecting and characterizing defects in GFRP composite by using thermal nondestructive testing / A. O. Chulkov, D. A. Nesteruk, V. P. Vavilov [et al.]
Уровень набора: Infrared Physics and TechnologyЯзык: английский.Страна: .Резюме или реферат: 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)..Примечания о наличии в документе библиографии/указателя: [References: 29 tit.].Аудитория: .Тематика: 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 Ресурсы он-лайн:Щелкните здесь для доступа в онлайнНет реальных экземпляров для этой записи
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[References: 29 tit.]
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).
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