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HomeMaterials Science ForumMaterials Science Forum Vol. 1094A Machine Learning Model for Flaw Identification...

A Machine Learning Model for Flaw Identification in Fibre-Reinforced Composites

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Abstract:

A Haar cascade classifier is a machine learning (ML) algorithm used for object detection. In this paper, the Haar algorithm is introduced in the context of a non-destructive evaluation of fibrereinforced composite (FRC) structures. The Haar learning model is used for flaw identification from thermal images. Thermal images are created from cross-ply (CP) carbon fibre-reinforced laminates with flat-bottomed holes (6–10 mm) of different depths from the surface (0.5–1.5 mm). After training is complete, the model successfully detects similar artificial flaws in previously unseen thermal images. In doing so, the feasibility of Haar classifiers for automatic evaluation of FRCs is established.

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Info:

Periodical:

Materials Science Forum (Volume 1094)

Pages:

5-10

DOI:

https://doi.org/10.4028/p-IgDB3j

Citation:

Cite this paper

Online since:

July 2023

Authors:

Wayne Hall*, Andreas Kromik, Brenton Miller, Ian Underhill, Zia Javanbakht

Keywords:

Fibre-Reinforced Composites, Haar Cascade, Machine Learning, NDT, Thermography

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* - Corresponding Author

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