Quantitative detection of the width and depth of subsurface defects in curved structures using ultrasonic Rayleigh waves

Rayleigh waves are highly sensitive to surface and subsurface defects. However, the varying surface profiles of complex structures induce constant changes in the Rayleigh waveform. Furthermore, Rayleigh waves encompass multi-dimensional characteristics of subsurface defects, significantly increasing the difficulty of quantitatively detecting the sizes of subsurface defects in different dimensions. This paper presents a machine learning (ML)-based method for quantitatively detecting subsurface defect depth and width in curved structures using laser-generated Rayleigh waves. An experimentally validated finite element model is first established to construct a dataset of Rayleigh wave signals labeled with multidimensional subsurface defect sizes. The dataset is augmented from 188 to 368 samples using the Mix-up algorithm, with additional experimental signals incorporated. Feature parameters of Rayleigh waves are extracted in both time and frequency domains through waveform analysis and sliding window wavelet transform (SWT). These features are used to train ML models: Gaussian Process (GP), Extreme Gradient Boosting (XGBoost), and Random Forest (RF). Comparative results demonstrate GP achieves the highest prediction accuracy for subsurface defect depth and width on the test set, reaching 98.64% and 97.73%, respectively. The study highlights the integration of experimental and simulated data in training, which reduces costs while enhancing model robustness in practical applications.