Image-Based Flaw Identification Using Convolutional Neural Network

Abstract

Flaw detection in structures is crucial for ensuring structural integrity and safety across various engineering applications. Traditional nondestructive evaluation (NDE) techniques often face challenges in accurately identifying and characterizing flaws, particularly when dealing with complex geometries and strain fields. In this study, we propose a deep learning-based approach utilizing convolutional neural networks (CNNs) for the regression-based parameter identification of flaws in structures. Specifically, we focus on identifying and characterizing circular flaws and cracks. The photoelastic fringe patterns of the flawed structure are used for training and testing the model and are generated using the quadtree-based scaled boundary finite element method (SBFEM), which provides high-fidelity images. The proposed CNN model is trained on these fringe images to learn the intricate patterns associated with different types of flaws and to regress the geometric parameters of the flaws accurately. The results demonstrate that our approach achieves high accuracy, with the CNN model's predictions for both circular flaws and cracks approaching 99%, showcasing the potential of deep learning in advancing NDE methods.

Keywords

convolutional neural network / flaw detection / photoelastic fringe patterns / scaled boundary finite element method

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2025 The Author(s). International Journal of Mechanical System Dynamics published by John Wiley & Sons Australia, Ltd on behalf of Nanjing University of Science and Technology.