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

Bibliographic Details
Parent link:Journal of Nondestructive Evaluation
Vol. 42, iss. 1.— 2023.— 20, 16 p.
Other Authors: Chulkov A. O. Arseniy Olegovich, Vavilov V. P. Vladimir Platonovich, Shagdyrov B. I. Bator Ilyich, Kladov D. Dmitry
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
Language:English
Published: 2023
Subjects:
электронный ресурс
труды учёных ТПУ
thermal NDT
defect characterization
composite-metal structure
neural network
line scan thermography
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

Similar Items

Similar Items