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Automated detection and characterization of defects in composite-metal structures by using active infrared thermography

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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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The authors are grateful to Douglas Burleigh for multiyear collaboration and useful discussion on practical aspects of thermal NDT. This study was supported by the Russian Scientific Foundation grant # 22-29-01469.

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AC: conducted modeling and experimental studies. VV: wrote the manuscript text. BS: prepared the set of the reference samples. DK built the experimental setup.

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Correspondence to A. O. Chulkov.

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Chulkov, A.O., Vavilov, V.P., Shagdyrov, B.I. et al. Automated detection and characterization of defects in composite-metal structures by using active infrared thermography. J Nondestruct Eval 42, 20 (2023).

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