Automated detection and characterization of defects in composite-metal structures by using active infrared thermography
| Parent link: | Journal of Nondestructive Evaluation Vol. 42, iss. 1.— 2023.— 20, 16 p. |
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| Other Authors: | , , , |
| Summary: | 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 AM_Agreement |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://doi.org/10.1007/s10921-023-00929-x |
| Format: | Electronic Book Chapter |
| KOHA link: | https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=669258 |