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

Podrobná bibliografie
Parent link:Journal of Nondestructive Evaluation
Vol. 42, iss. 1.— 2023.— 20, 16 p.
Další autoři: Chulkov A. O. Arseniy Olegovich, Vavilov V. P. Vladimir Platonovich, Shagdyrov B. I. Bator Ilyich, Kladov D. Dmitry
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
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 
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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 
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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 
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