An Automated Algorithm for Constructing Maps of Defects in Active Thermal Testing

Результат исследований: Материалы для журналаСтатья

Выдержка

Abstract: The algorithm makes it possible to simplify the procedure for processing results of the thermal testing aimed at both revealing latent defects and evaluating their transverse dimensions and shape. Applying this algorithm requires certain participation and experience of the thermography operator, as well as preliminary preparation of initial data by using techniques that increase the signal-to-noise ratio. The algorithm includes selection of defective zones on the thermogram of the test object, automated identification of points with extreme signals, and a pixel-by-pixel threshold analysis of the zones adjacent to these points, culminating in the construction of binary defect maps.

Язык оригиналаАнглийский
Страницы (с-по)617-621
Число страниц5
ЖурналRussian Journal of Nondestructive Testing
Том55
Номер выпуска8
DOI
СостояниеОпубликовано - 1 авг 2019

Отпечаток

Defects
defects
Testing
Pixels
pixels
thermograms
Signal to noise ratio
signal to noise ratios
operators
preparation
thresholds
Processing
Hot Temperature

ASJC Scopus subject areas

  • Materials Science(all)
  • Condensed Matter Physics
  • Mechanics of Materials
  • Mechanical Engineering

Цитировать

An Automated Algorithm for Constructing Maps of Defects in Active Thermal Testing. / Chulkov, A. O.; Nesteruk, D. A.; Vavilov, V. P.

В: Russian Journal of Nondestructive Testing, Том 55, № 8, 01.08.2019, стр. 617-621.

Результат исследований: Материалы для журналаСтатья

@article{0f2cf6c0b27e45589ce63f8e5d6998d7,
title = "An Automated Algorithm for Constructing Maps of Defects in Active Thermal Testing",
abstract = "Abstract: The algorithm makes it possible to simplify the procedure for processing results of the thermal testing aimed at both revealing latent defects and evaluating their transverse dimensions and shape. Applying this algorithm requires certain participation and experience of the thermography operator, as well as preliminary preparation of initial data by using techniques that increase the signal-to-noise ratio. The algorithm includes selection of defective zones on the thermogram of the test object, automated identification of points with extreme signals, and a pixel-by-pixel threshold analysis of the zones adjacent to these points, culminating in the construction of binary defect maps.",
keywords = "automated testing, composite materials, infrared thermography, thermal testing, transverse defect size",
author = "Chulkov, {A. O.} and Nesteruk, {D. A.} and Vavilov, {V. P.}",
year = "2019",
month = "8",
day = "1",
doi = "10.1134/S1061830919080035",
language = "English",
volume = "55",
pages = "617--621",
journal = "Russian Journal of Nondestructive Testing",
issn = "1061-8309",
publisher = "Maik Nauka-Interperiodica Publishing",
number = "8",

}

TY - JOUR

T1 - An Automated Algorithm for Constructing Maps of Defects in Active Thermal Testing

AU - Chulkov, A. O.

AU - Nesteruk, D. A.

AU - Vavilov, V. P.

PY - 2019/8/1

Y1 - 2019/8/1

N2 - Abstract: The algorithm makes it possible to simplify the procedure for processing results of the thermal testing aimed at both revealing latent defects and evaluating their transverse dimensions and shape. Applying this algorithm requires certain participation and experience of the thermography operator, as well as preliminary preparation of initial data by using techniques that increase the signal-to-noise ratio. The algorithm includes selection of defective zones on the thermogram of the test object, automated identification of points with extreme signals, and a pixel-by-pixel threshold analysis of the zones adjacent to these points, culminating in the construction of binary defect maps.

AB - Abstract: The algorithm makes it possible to simplify the procedure for processing results of the thermal testing aimed at both revealing latent defects and evaluating their transverse dimensions and shape. Applying this algorithm requires certain participation and experience of the thermography operator, as well as preliminary preparation of initial data by using techniques that increase the signal-to-noise ratio. The algorithm includes selection of defective zones on the thermogram of the test object, automated identification of points with extreme signals, and a pixel-by-pixel threshold analysis of the zones adjacent to these points, culminating in the construction of binary defect maps.

KW - automated testing

KW - composite materials

KW - infrared thermography

KW - thermal testing

KW - transverse defect size

UR - http://www.scopus.com/inward/record.url?scp=85074176969&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85074176969&partnerID=8YFLogxK

U2 - 10.1134/S1061830919080035

DO - 10.1134/S1061830919080035

M3 - Article

AN - SCOPUS:85074176969

VL - 55

SP - 617

EP - 621

JO - Russian Journal of Nondestructive Testing

JF - Russian Journal of Nondestructive Testing

SN - 1061-8309

IS - 8

ER -