Characterization of Crack Geometry via Inductive Thermography and Machine Learning

Inductive thermography provides a non-destructive approach for detecting and characterizing cracks in metallic components. This study introduces a method to assess crack geometry—depth and inclination angle—by combining inductive thermography with machine learning. Thermographic sequences from inductively heated cracked specimens were processed using various techniques, including Fourier transform, to generate phase images. A comparative analysis revealed that the fast Fourier transform (FFT) outperformed other methods, achieving the highest contrast-to-noise ratio (CNR) and effectively suppressing non-uniform heating effects. Phase profiles perpendicular to the crack, extracted at its midpoint, were used as input features. Two machine learning models were developed: one trained on simulated phase profiles to predict crack inclination angle, and a second to estimate crack depth based on the known angle and phase data. Validated against simulated datasets, the models demonstrated high accuracy, advancing the quantitative evaluation of crack geometry for structural integrity and predictive maintenance applications.