Automated detection and characterization of defects in composite-metal structures by using active infrared thermography / A. O. Chulkov, V. P. Vavilov, B. I. Shagdyrov, D. Kladov
Уровень набора: Journal of Nondestructive EvaluationЯзык: английский.Страна: .Резюме или реферат: Several composite-metal samples with artificial defects of varying size and depth were experimentally investigated to demonstrate effectiveness of using a line scan thermographic nondestructive testing in combination with a neural network in the automated procedure of defect detection and characterization. The proposed data processing algorithm allowed defect thermal characterization with a practically accepted accuracy up to 16% and 51% by defect depth and thickness respectively. Characterization results were presented as distributions of defect depth and thickness correspondingly called depthgram and thicknessgram. For training a neural network, it was suggested to prepare input data in the form of non-stationary temperature profiles processed by using the thermographic signal reconstruction method..Примечания о наличии в документе библиографии/указателя: [References: 21 tit.].Аудитория: .Тематика: электронный ресурс | труды учёных ТПУ | thermal NDT | defect characterization | composite-metal structure | neural network | line scan thermography Ресурсы он-лайн:Щелкните здесь для доступа в онлайнTitle screen
[References: 21 tit.]
Several composite-metal samples with artificial defects of varying size and depth were experimentally investigated to demonstrate effectiveness of using a line scan thermographic nondestructive testing in combination with a neural network in the automated procedure of defect detection and characterization. The proposed data processing algorithm allowed defect thermal characterization with a practically accepted accuracy up to 16% and 51% by defect depth and thickness respectively. Characterization results were presented as distributions of defect depth and thickness correspondingly called depthgram and thicknessgram. For training a neural network, it was suggested to prepare input data in the form of non-stationary temperature profiles processed by using the thermographic signal reconstruction method.
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