Optimizing input data for training an artificial neural network used for evaluating defect depth in infrared thermographic nondestructive testing; Infrared Physics and Technology; Vol. 102

Optimizing input data for training an artificial neural network used for evaluating defect depth in infrared thermographic nondestructive testing; Infrared Physics and Technology; Vol. 102

Bibliografske podrobnosti
Parent link:	Infrared Physics and Technology Vol. 102.— 2019.— [103047, 7 p.]
Corporate Authors:	Национальный исследовательский Томский политехнический университет Инженерная школа неразрушающего контроля и безопасности Центр промышленной томографии Научно-производственная лаборатория "Бетатронная томография крупногабаритных объектов", Национальный исследовательский Томский политехнический университет Инженерная школа неразрушающего контроля и безопасности Центр промышленной томографии Научно-производственная лаборатория "Тепловой контроль"
Drugi avtorji:	Chulkov A. O. Arseniy Olegovich, Nesteruk D. A. Denis Alekseevich, Vavilov V. P. Vladimir Platonovich, Moskovchenko A. I. Aleksey Igorevich, Saeed N. Numan, Omar M. A.
Izvleček:	Title screen Ten different sets of input data have been used for training and verification of the neural network intended for determining defect depth in infrared thermographic nondestructive testing. The input data sets included raw temperature data, polynomial fitting, principle component analysis, Fourier transform and others. A minimum error (up 0.02 mm for defects in CFRP at depths from 0.5 to 2.5 mm) has been achieved by using polynomial fitting in logarithmic coordinates with further computation of the first temperature derivatives (the TSR technique), and close results have been obtained by processing raw data with the PCA technique. Both techniques require no use of reference points. Режим доступа: по договору с организацией-держателем ресурса
Jezik:	angleščina
Izdano:	2019
Teme:	электронный ресурс труды учёных ТПУ infrared thermographic testing neural network data processing defect depth composite material инфракрасный контроль нейронные сети обработка данных глубина дефекты композитные материалы
Online dostop:	https://doi.org/10.1016/j.infrared.2020.103289
Format:	Elektronski Book Chapter
KOHA link:	https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=663088

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