To ensure the thermal stability of aero-engine blades under high temperature and harsh service environments, it is necessary to quickly and accurately evaluate the thickness of thermal barrier coatings (TBCs). In this work, it was proposed to use the terahertz nondestructive testing (NDT) technique combined with the hybrid machine learning algorithm to measure the thickness of TBCs. The finite difference time-domain (FDTD) method was used to model the optical propagation characteristics of TBC samples with different thicknesses (101–300 μm) in the frequency band. To make the terahertz time-domain signal obtained simulation more realistic, uniform white noise was added to the simulation data and wavelet denoising was conducted to mimic the real testing environment. Principal components analysis (PCA) algorithm and whale optimization algorithm (WOA) combined with an optimized Elman neural network algorithm was employed to set up the hybrid machine learning model. Finally, the hybrid thickness regression prediction model shows low error, high accuracy, and an exceptional coefficient of determination R² of 0.999. It was demonstrated that the proposed hybrid algorithm could meet the thickness evaluation requirements. Meanwhile, a novel, efficient, safe, and accurate terahertz nondestructive testing method has shown great potential in the evaluation of structural integrity of thermal barrier coatings in the near future.

Microstructural features have a vital effect on the comprehensive performance of thermal barrier coatings (TBCs) and highly depend on the thermal spray processing parameters. Herein, a novel hybrid machine-learning method was proposed to predict the microstructural features of TBCs using thermal spray processing parameters based on a support vector machine method optimized by the cuckoo search algorithm (CS-SVM). In this work, atmospheric-plasma-sprayed (APS) TBCs samples with multifarious microstructural features were acquired by modifying the spray powder size, spray distance, and spray power during thermal spray processing. The processing parameters were used as the inputs for the CS-SVM model. Then, the porosity, the pore-to-crack ratio, the maximum Feret’s diameter, the aspect ratio, and the circularity were counted and treated as the targets for the CS-SVM model. After optimization and training procedure of the CS-SVM model, the predicted results were compared to the results of experimental data, as a result, the squared correlation coefficient (R²) of CS-SVM model showed that the prediction accuracy reached by over 95%, and the root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) were less than 0.1, which also verified the reliability of the CS-SVM model. Finally, this study proposed a novel and efficient microstructural feature prediction that could be potentially employed to improve the performance of TBCs in service.

The deterioration of the mechanical properties of metal induced by hydrogen absorption threatens the safety of the equipment serviced in hydrogen environments. In this study, the hydrogen concentration distribution in 2.25Cr-1Mo-0.25V steel after hydrogen charging was analyzed following the hydrogen permeation and diffusion model. The diffusible hydrogen content in the 1-mm-thick specimen and its influence on the mechanical properties of the material were investigated by glycerol gas collecting test, static hydrogen charging tensile test, scanning electron microscopy (SEM) test, and microhardness test. The results indicate that the content of diffusible hydrogen tends to be the saturation state when the hydrogen charging time reaches 48 h. The simulation results suggest that the hydrogen concentration distribution can be effectively simulated by ABAQUS and the method can be used to analyze the hydrogen concentration in the material with complex structures or containing multiple microstructures. The influence of hydrogen on the mechanical properties is that the elongation of this material is reduced and the diffusible hydrogen will cause a decrease in the fracture toughness of the material, and thus hydrogen embrittlement (HE) will occur. Moreover, the Young’s modulus E and microhardness are increased due to hydrogen absorption, and the variation value is related to the hydrogen concentration introduced into the specimen.

Grit blasting as a pretreatment process for the substrate surface before thermal spraying is of great importance for assuring the service performance of thermal spraying coatings. In this work, a novel hybrid artificial neural network (ANN) was presented to optimize the grit blasting process to improve the structural properties and corrosion resistance performance of thermal spraying coatings. Different grit blasting process parameters were combined to pretreat the substrate surface, and the corresponding surface roughness, interface adhesion strength and corrosion resistance performance were obtained. Hence, a backpropagation (BP) neural network model optimized by the genetic algorithm (GA) was presented to address the poor regression roughness and accuracy of the traditional fitting models; the grit blasting processing parameters were utilized as the inputs for the GA–BP model; the structural properties and the corrosion resistance performance were used as the outputs. The correlation coefficient R reached and exceeded 0.90, and three error values were less than 1.75 on the prediction of the service performance of random samples. All these indicators demonstrated convincingly that the obtained hybrid artificial neural network models possessed good prediction performance, and this innovative and time-saving grit blasting process optimization approach could be potentially employed to improve the comprehensive service performance of thermal spraying coatings.

Thermal barrier coatings (TBCs) are usually subjected to the combined action of compressive stress, tensile stress, and bending shear stress, resulting in the interfacial delamination of TBCs, and finally causing the ceramic top coat to peel off. Hence, it is vital to detect the early-stage subcritical delamination cracks. In this study, a novel hybrid artificial neural network combined with the terahertz nondestructive technology was presented to predict the thickness of interface delamination in the early stage. The finite difference time domain (FDTD) algorithm was used to obtain the raw terahertz time-domain signals of 32 TBCs samples with various thicknesses of interface delamination, not only that, the influence of roughness and the thickness of the ceramic top layer were considered comprehensively when modeling. The stationary wavelet transform (SWT) and principal component analysis (PCA) methods were employed to extract the signal features and reduce the data dimensions before modeling, to make the cumulative contribution rate reach 100%, the first 31 components of the SWT detail data was used as the input data during modeling. Finally, a back propagation (BP) neural network method optimized by the genetic algorithm (GA-BP) was proposed to set up the interface delamination thickness prediction model. As a result, the root correlation coefficient R² reached over 0.95, the various errors—including the mean square error, mean squared percentage error, and mean absolute percentage error—were less than or equal to 0.53. All these indicators proved that the trained hybrid SWT-PCA-GA-BP model had excellent prediction performance and high accuracy. Finally, this work proposed a novel and convenient interface delamination evaluation method that could also be potentially utilized to evaluate the structural integrity of TBCs.

Accurately characterizing the internal porosity rate of thermal barrier coatings (TBCs) was essential for prolonging their service life. This work concentrated on atmospheric plasma spray (APS)-prepared TBCs and proposed the utilization of terahertz non-destructive detection technology to evaluate their internal porosity rate. The internal porosity rates were ascertained through a metallographic analysis and scanning electron microscopy (SEM), followed by the reconstruction of the TBC model using a four-parameter method. Terahertz time-domain simulation data corresponding to various porosity rates were generated employing the time-domain finite difference method. In simulating actual test signals, white noise with a signal-to-noise ratio of 10 dB was introduced, and various wavelet transforms were utilized for denoising purposes. The effectiveness of different signal processing techniques in mitigating noise was compared to extract key features associated with porosity. To address dimensionality challenges and further enhance model performance, kernel principal component analysis (kPCA) was employed for data processing. To tackle issues related to limited sample sizes, this work proposed to use the Siamese neural network (SNN) and generative adversarial network (GAN) algorithms to solve this challenge in order to improve the generalization ability and detection accuracy of the model. The efficacy of the constructed model was assessed using multiple evaluation metrics; the results indicate that the novel hybrid WT-kPCA-GAN model achieves a prediction accuracy exceeding 0.9 while demonstrating lower error rates and superior predictive performance overall. Ultimately, this work presented an innovative, convenient, non-destructive online approach that was safe and highly precise for measuring the porosity rate of TBCs, particularly in scenarios involving small sample sizes facilitating assessments regarding their service life.