Effective control of the thickness of the hot-rolled oxide scale on the surface of the steel strip is very vital to ensure the surface quality of steel products. Hence, terahertz nondestructive technology was proposed to measure the thickness of thin oxide scale. The finite difference time domain (FDTD) numerical simulation method was employed to obtain the terahertz time-domain simulation data of oxide scale with various thickness (0–15 μm). Added Gaussian white noise with a Signal Nosie Reduction (SNR) of 10 dB was used when simulating real test signals, using four wavelet denoising methods to reduce noise and to compare their effectiveness. Two machine learning algorithms were adopted to set up models to achieve this goal, including the classical back-propagation (BP) neural network algorithm and the novel extreme learning machine (ELM) algorithm. The principal component analysis (PCA) algorithm and particle swarm optimization (PSO) algorithm were combined to reduce the dimensions of the terahertz time-domain data and improve the robustness of the machine learning model. It could be clearly seen that the novel hybrid PCA-PSO-ELM model possessed excellent prediction performance. Finally, this work proposed a novel, convenient, online, nondestructive, noncontact, safety and high-precision thin oxide scale thickness measuring method that could be employed to improve the surface quality of iron and steel products.

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.

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.