Application of Machine Learning in Terahertz-Based Nondestructive Testing of Thermal Barrier Coatings with High-Temperature Growth Stresses

The gradual growth of oxides inside thermal barrier coatings is a key factor leading to the degradation of thermal barrier coating performance until its failure, and accurate monitoring of the growth stress during this process is crucial to ensure the long-term stable operation of engines. In this study, terahertz time-domain spectroscopy was introduced as a new method to characterize the growth stress in thermal barrier coatings. By combining metallographic analysis and scanning electron microscope (SEM) observation techniques, the real microstructure of the oxide layer was obtained, and an accurate simulation model of the oxide growth was constructed on this basis. The elastic solutions of the thermally grown oxide layer of thermal insulation coatings were obtained by using the controlling equations in the rate-independent theoretical model, and the influence of the thickness of the thermally grown oxide (TGO) layer on the stress distribution was explored. Based on experimental data, multidimensional 3D numerical models of thermal barrier coatings with different TGO thicknesses were constructed, and the terahertz time-domain responses of oxide coatings with different thicknesses were simulated using the time-domain finite difference method to simulate the actual inspection scenarios. During the simulation process, white noise with signal-to-noise ratios of 10 dB to 20 dB was embedded to approximate the actual detection environment. After adding the noise, wavelet transform (WT) was used to reduce the noise in the data. The results showed that the wavelet transform had excellent noise reduction performance. For the problems due to the large data volume and small sample data after noise reduction, local linear embedding (LLE) and kernel-based extreme learning machine (KELM) were used, respectively, and the kernel function was optimized using the gray wolf optimization (GWO) algorithm to improve the model’s immunity to interference. Experimental validation showed that the proposed LLE-GWO-KELM hybrid model performed well in predicting the TGO growth stress of thermal insulation coatings. In this study, a novel, efficient, nondestructive, online, and high-precision measurement method for the growth in TGO stress of thermal barrier coatings was developed, which provides reliable technical support for evaluating the service life of thermal barrier coatings.