Automated procedure for detecting and characterizing defects in GFRP composite by using thermal nondestructive testing

A. O. Chulkov; D. A. Nesteruk; V. P. Vavilov; B. Shagdirov; M. Omar; A. O. Siddiqui; Y. L.V.D. Prasad

doi:10.1016/j.infrared.2021.103675

Automated procedure for detecting and characterizing defects in GFRP composite by using thermal nondestructive testing

A. O. Chulkov
, D. A. Nesteruk
, V. P. Vavilov
, B. Shagdirov
, M. Omar
, A. O. Siddiqui
, Y. L.V.D. Prasad

Management Science & Engineering

Research output: Contribution to journal › Article › peer-review

7 Scopus citations

Abstract

The paper describes the concept of an automated defect characterization procedure by using infrared nondestructive testing of glass fiber reinforced composite. The proposed algorithms have allowed determination of defect depth, lateral dimensions and area, as well as coordinates of defect centers. The algorithms are based on the use of the neural network trained on both experimental and theoretical temperature profiles. An acceptable for practice accuracy of defect characterization has been obtained on the experimental data (0–15% by defect depth and 26–139% by defect area).

Original language	British English
Article number	103675
Journal	Infrared Physics and Technology
Volume	114
DOIs	https://doi.org/10.1016/j.infrared.2021.103675
State	Published - May 2021

Keywords

Active infrared thermography
Automated defect characterization
Automated defect detection
Data processing
Glass fiber reinforced composite
Neural network

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10.1016/j.infrared.2021.103675

Cite this

@article{f5a241b150b54d4eae2c48d769f3b9ac,

title = "Automated procedure for detecting and characterizing defects in GFRP composite by using thermal nondestructive testing",

abstract = "The paper describes the concept of an automated defect characterization procedure by using infrared nondestructive testing of glass fiber reinforced composite. The proposed algorithms have allowed determination of defect depth, lateral dimensions and area, as well as coordinates of defect centers. The algorithms are based on the use of the neural network trained on both experimental and theoretical temperature profiles. An acceptable for practice accuracy of defect characterization has been obtained on the experimental data (0–15\% by defect depth and 26–139\% by defect area).",

keywords = "Active infrared thermography, Automated defect characterization, Automated defect detection, Data processing, Glass fiber reinforced composite, Neural network",

author = "Chulkov, \{A. O.\} and Nesteruk, \{D. A.\} and Vavilov, \{V. P.\} and B. Shagdirov and M. Omar and Siddiqui, \{A. O.\} and Prasad, \{Y. L.V.D.\}",

note = "Funding Information: This study was supported by the Russian Foundation for Basic Research grant \#19-29-13004/19 (algorithms and test methodology), Russian State Project “Science” FSWW-2020-0014 (experimentation) and Tomsk Polytechnic University Competitiveness Enhancement Program (equipment). The reference sample was presented by Advanced Systems Laboratory, India. Publisher Copyright: {\textcopyright} 2021 Elsevier B.V.",

year = "2021",

month = may,

doi = "10.1016/j.infrared.2021.103675",

language = "British English",

volume = "114",

journal = "Infrared Physics and Technology",

issn = "1350-4495",

publisher = "Elsevier B.V.",

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TY - JOUR

T1 - Automated procedure for detecting and characterizing defects in GFRP composite by using thermal nondestructive testing

AU - Chulkov, A. O.

AU - Nesteruk, D. A.

AU - Vavilov, V. P.

AU - Shagdirov, B.

AU - Omar, M.

AU - Siddiqui, A. O.

AU - Prasad, Y. L.V.D.

N1 - Funding Information: This study was supported by the Russian Foundation for Basic Research grant #19-29-13004/19 (algorithms and test methodology), Russian State Project “Science” FSWW-2020-0014 (experimentation) and Tomsk Polytechnic University Competitiveness Enhancement Program (equipment). The reference sample was presented by Advanced Systems Laboratory, India. Publisher Copyright: © 2021 Elsevier B.V.

PY - 2021/5

Y1 - 2021/5

N2 - The paper describes the concept of an automated defect characterization procedure by using infrared nondestructive testing of glass fiber reinforced composite. The proposed algorithms have allowed determination of defect depth, lateral dimensions and area, as well as coordinates of defect centers. The algorithms are based on the use of the neural network trained on both experimental and theoretical temperature profiles. An acceptable for practice accuracy of defect characterization has been obtained on the experimental data (0–15% by defect depth and 26–139% by defect area).

AB - The paper describes the concept of an automated defect characterization procedure by using infrared nondestructive testing of glass fiber reinforced composite. The proposed algorithms have allowed determination of defect depth, lateral dimensions and area, as well as coordinates of defect centers. The algorithms are based on the use of the neural network trained on both experimental and theoretical temperature profiles. An acceptable for practice accuracy of defect characterization has been obtained on the experimental data (0–15% by defect depth and 26–139% by defect area).

KW - Active infrared thermography

KW - Automated defect characterization

KW - Automated defect detection

KW - Data processing

KW - Glass fiber reinforced composite

KW - Neural network

UR - https://www.scopus.com/pages/publications/85101723329

U2 - 10.1016/j.infrared.2021.103675

DO - 10.1016/j.infrared.2021.103675

M3 - Article

AN - SCOPUS:85101723329

SN - 1350-4495

VL - 114

JO - Infrared Physics and Technology

JF - Infrared Physics and Technology

M1 - 103675

ER -

Automated procedure for detecting and characterizing defects in GFRP composite by using thermal nondestructive testing

Abstract

Keywords

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