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. |
|---|---|
| Další autoři: | , , , |
| Shrnutí: | 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 |
| Jazyk: | angličtina |
| Vydáno: |
2023
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| Témata: | |
| On-line přístup: | https://doi.org/10.1007/s10921-023-00929-x |
| Médium: | Elektronický zdroj Kapitola |
| KOHA link: | https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=669258 |
MARC
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| 200 | 1 | |a Automated detection and characterization of defects in composite-metal structures by using active infrared thermography |f A. O. Chulkov, V. P. Vavilov, B. I. Shagdyrov, D. Kladov | |
| 203 | |a Текст |c электронный |b визуальный | ||
| 283 | |a online_resource |2 RDAcarrier | ||
| 320 | |a References: 21 tit | ||
| 330 | |a 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 | ||
| 371 | 1 | |a AM_Agreement | |
| 461 | 1 | |t Journal of Nondestructive Evaluation | |
| 463 | 1 | |t Vol. 42, iss. 1 |v 20, 16 p. |d 2023 | |
| 610 | 1 | |a электронный ресурс | |
| 610 | 1 | |a труды учёных ТПУ | |
| 610 | 1 | |a thermal NDT | |
| 610 | 1 | |a defect characterization | |
| 610 | 1 | |a composite-metal structure | |
| 610 | 1 | |a neural network | |
| 610 | 1 | |a line scan thermography | |
| 701 | 1 | |a Chulkov |b A. O. |c specialist in the field of non-destructive testing |c Deputy Director for Scientific and Educational Activities; acting manager; Senior Researcher, Tomsk Polytechnic University, Candidate of Technical Sciences |f 1989- |g Arseniy Olegovich |3 (RuTPU)RU\TPU\pers\32220 |9 16220 | |
| 701 | 1 | |a Vavilov |b V. P. |c Specialist in the field of dosimetry and methodology of nondestructive testing (NDT) |c Doctor of technical sciences (DSc), Professor of Tomsk Polytechnic University (TPU) |f 1949- |g Vladimir Platonovich |3 (RuTPU)RU\TPU\pers\32161 |9 16163 | |
| 701 | 1 | |a Shagdyrov |b B. I. |c specialist in the field of non-destructive testing |c engineer of Tomsk Polytechnic University |f 1995- |g Bator Ilyich |3 (RuTPU)RU\TPU\pers\47532 |9 23046 | |
| 701 | 1 | |a Kladov |b D. |c specialist in the field of non-destructive testing |c engineer of Tomsk Polytechnic University |f 1996- |g Dmitry |3 (RuTPU)RU\TPU\pers\47534 |9 23048 | |
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| 856 | 4 | |u https://doi.org/10.1007/s10921-023-00929-x |z https://doi.org/10.1007/s10921-023-00929-x | |
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