Quantitative Detection of CMAS Thickness on Thermal Barrier Coatings via Terahertz Technology Combined with Machine Learning

Modern turbine engines, when operating at high temperatures, can inhale calcium–magnesium–alumina–silicate particles (CaO-MgO-Al2O3-SiO2, CMAS) from the air, which can erode the thermal barrier coatings on the blade surface, affecting the service life of the thermal barrier coatings and, in severe cases, leading to premature blade failure. Therefore, it is of great significance to effectively detect the thickness of CMAS deposited on the surface of the thermal barrier coatings at an early stage of CMAS erosion to ensure the high-temperature structural integrity of the hot-end components of aeroengines. Based on this, this study proposes a method combining terahertz time-domain spectroscopy technology and a hybrid machine learning algorithm for the quantitative detection of the thickness of CMAS on the surface of thermal barrier coatings. Firstly, the terahertz time-domain spectroscopy experimental data of CMAS were obtained using a terahertz experimental system, and the refractive index and absorption coefficient of CMAS in the terahertz frequency band were calculated. The FDTD method, Gaussian noise addition, and wavelet denoising processing were combined to further simulate the terahertz detection process of thermal barrier coatings with different thicknesses of CMAS attached to the surface under high-temperature conditions, and the terahertz simulation detection data were obtained. Principal component analysis (PCA) was used to reduce the dimensionality of the original experimental and simulation data, and a support vector machine (SVM) model integrating PCA and bacterial foraging optimization (BFO) algorithm was constructed. The research results show that the integrated model exhibits excellent performance in predicting the thickness of CMAS, with a correlation coefficient of 0.95, and the mean absolute error (MAE) and root mean square error (RMSE) are 0.13 μm and 0.46 μm, respectively. This study provides a new high-precision method for non-destructive detection of the thickness of CMAS on the surface of thermal barrier coatings, which has certain engineering application value for ensuring the service performance of thermal barrier coatings under harsh service conditions. Although the current method is based on simulated and experimental data under controlled conditions, it has the potential to be developed into an in situ monitoring strategy in the future, enabling real-time assessment of CMAS thickness on the coating surface during engine operation.