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

Research output: Contribution to journalArticlepeer-review

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 languageEnglish
Article number103675
JournalInfrared Physics and Technology
Volume114
DOIs
Publication statusPublished - May 2021

Keywords

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

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Condensed Matter Physics

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