Automated detection and characterization of defects in composite-metal structures by using active infrared thermography; Journal of Nondestructive Evaluation; Vol. 42, iss. 1

Automated detection and characterization of defects in composite-metal structures by using active infrared thermography; Journal of Nondestructive Evaluation; Vol. 42, iss. 1

Xehetasun bibliografikoak
Parent link:	Journal of Nondestructive Evaluation Vol. 42, iss. 1.— 2023.— 20, 16 p.
Beste egile batzuk:	Chulkov A. O. Arseniy Olegovich, Vavilov V. P. Vladimir Platonovich, Shagdyrov B. I. Bator Ilyich, Kladov D. Dmitry
Gaia:	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
Hizkuntza:	ingelesa
Argitaratua:	2023
Gaiak:	электронный ресурс труды учёных ТПУ thermal NDT defect characterization composite-metal structure neural network line scan thermography
Sarrera elektronikoa:	https://doi.org/10.1007/s10921-023-00929-x
Formatua:	Baliabide elektronikoa Liburu kapitulua
KOHA link:	https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=669258

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