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

    4 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 languageBritish English
    Article number103675
    JournalInfrared Physics and Technology
    Volume114
    DOIs
    StatePublished - May 2021

    Keywords

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

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