A Hybrid Machine Learning Framework for Quantitative Defect Sizing in Eddy Current Testing
Please login to view abstract download link
Eddy Current Testing (ECT) is a well-established method of Nondestructive Testing (NDT) for ensuring the integrity of conductive materials. While defect detection is well-established, the transition to accurate quantitative sizing-predicting the precise dimensions of a flaw-remains a significant challenge, yet it is crucial for structural health assessment. This transition is hindered by two main obstacles: the difficulty in obtaining high-fidelity ground-truth data for model training, and the inherent class imbalance of inspection data, where defect-free regions vastly outnumber defective ones, biasing traditional machine learning models. This study proposes a comprehensive framework to overcome both challenges. First, we introduce a geometry-guided labeling methodology that leverages a spherical parameterization of the defect to create a dense, point-by-point depth map, ensuring highly accurate training data. Second, we propose a hybrid classification-regression architecture. This model first employs a Gradient Boosting classifier to accurately distinguish between defective and non-defective signal regions. Subsequently, a specialized Random Forest regressor, trained exclusively on data from defective regions, performs the final depth prediction. This two-stage approach prevents the regression model from being biased by the overwhelming number of zero-depth samples. The proposed hybrid model's performance was rigorously evaluated and compared against both a standard direct-regression model. The results demonstrate the best performance of the hybrid architecture, achieving a significant improvement in predictive accuracy for defect depth. The primary contribution of this work is an integrated methodology that combines physics-based geometric labeling with a specialized machine learning architecture, offering a robust and accurate solution for quantitative defect characterization in real-world NDT applications.