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.

With the development of high-efficiency gas turbine engines and increasing inlet temperatures, the performance of thermal barrier coatings (TBCs) for hot-section components has been more severely challenged. The doping of multi-element rare earth elements significantly improves the thermodynamic properties and chemical compatibility of thermal barrier coatings so that the application performance of coatings in high-temperature environments is enhanced considerably. In this work, the doped coatings prepared by REYSZ (RE = La, Sm, Nd) were investigated and characterized in terms of crystal structure, elastic properties, and thermal–mechanical properties based on the first-principles approach, combined with various empirical and semi-empirical formulations, and a predictive model for resistance to CMAS corrosion based on machine learning approaches. The results showed that the tetragonal phase REYSZ material was mechanically stable, had a large strain damage tolerance, and was not easy to fracture under applied loads and thermal shocks. In terms of CMAS corrosion resistance, the NdYSZ interfacial model had a lower surface energy (3.130 J/m²) and Griffith fracture energy (6.934 J/m²) compared with the conventional YSZ model, and Nd₂O₃ had the potential to improve the CMAS corrosion resistance of zirconia-based material for thermal barrier coatings. By evaluating the machine learning prediction models, the regression coefficients of the two algorithms were 0.9627 and 0.9740, and both these two prediction models showed high prediction accuracy and strong robustness. Ultimately, this work presented a novel mechanism–data hybrid method, which would facilitate the efficient development of TBC new materials for anti-CMAS corrosion.

The spraying process affects the microstructure and service life of thermal barrier coatings. In this work, by changing the plasma-spraying process, the second-phase micron-agglomerated particles were introduced to prepare embedded micron-agglomerated particle thermal barrier coatings. Conventional thermal barrier coatings and embedded micron-agglomerated particle thermal barrier coatings were prepared by setting spraying process parameters with different powder feeding rates and distances between powder feeders. Sintering experiments and thermal cycling experiments were carried out on conventional thermal barrier coatings and embedded micron-agglomerated particle thermal barrier coatings. The effects of spraying process parameters on the microstructure and thermal cycle life of embedded micron-agglomerated particle thermal barrier coatings were systematically studied. Three machine learning models of BP, SVM, and GA-SVM were established to evaluate the relationship between spraying parameters and coating microstructure and thermal cycle life. The results show that the sintering resistance of the new thermal barrier coating is 500% higher than that of the conventional thermal barrier coating. When the distance between the two powder feeders is 35 mm and the powder feeder 1 rate is 12 g/min, the thermal cycle life of the coating is the best. When using machine learning model evaluation, the evaluation results of the three machine learning models have certain accuracy. Among them, the GA-SVM machine learning model has the best prediction effect and the smallest prediction error. The results of this paper provide a new paradigm for further preparation of thermal barrier coatings with high performance and long life.

Food security serves as a vital cornerstone for social stability. As one of the most important staple crops globally, the quality and geographical origin of rice are directly associated with consumer health. Traditional methods for classifying rice by origin and variety rely on sensory evaluation and manual inspection, which are subject to uncertainty and human error. To address this, this paper proposes a method for classifying rice by origin and variety based on terahertz time-domain spectroscopy. Terahertz technology features the advantages of non-destructive, high-sensitivity and non-contact detection, making it well-suited for food detection. This study employs terahertz time-domain spectroscopy combined with machine learning modeling methods, using 20 types of rice as the subject of investigation, with a focus on modeling and analyzing four representative samples. Refractive index and absorption coefficient were extracted through preprocessing methods including Savitzky–Golay convolution smoothing, wavelet denoising and moving average smoothing. Modeling, classification, and detection were implemented using principal component analysis, partial least squares discriminant analysis, and least-squares support vector machine. The experimental results indicate that principal component analysis (PCA) alone performs poorly in classification tasks. However, a classification model combining PCA for dimensionality reduction with a least-squares support vector machine (SVM), following Savitzky–Golay smoothing, demonstrated the best performance, achieving a prediction accuracy of 93.3%. In an extended test involving 20 samples, the model achieved an identification accuracy of 89.6%. Quantitative metrics demonstrate the feasibility of using terahertz technology combined with optimized machine learning algorithms for classifying rice by origin and variety.

The distribution morphology and density of micro-agglomerated particles are the main microstructural characteristics of embedded micro-agglomerated particle thermal barrier coatings. The study of their effect on the sintering resistance of coatings can help to further improve the service life of thermal barrier coatings. Strain tolerance and thermal insulation performance are important evaluation indicators for the sintering resistance of thermal barrier coatings. In this study, embedded micro-agglomerated particle thermal barrier coatings were prepared by plasma spraying, and the distribution morphology and density of micro-agglomerated particles were analyzed and counted. Different simulation models were established to analyze the compressive stress and thermal stress of the coating, as well as the influence of the microstructure characteristics on the strain tolerance and thermal insulation performance of the coating. A machine learning model was established to evaluate the nonlinear relationship between the microstructure characteristics of the coating and its strain tolerance and thermal insulation performance. The results show that the horizontal angle of the micro-agglomerated particles in the coating has the most significant effect on the sintering resistance and is predicted using the PSO-SVM machine learning model. The predicted effects are the most important, and the coefficients of determination for strain tolerance and thermal insulation are as high as 0.988 and 0.945, respectively, indicating a strong correlation between the predicted and actual values. This research technique used experimental research-simulation computation-machine learning can be used to optimize the microstructure of coatings and guide the preparation of high-performance thermal barrier coatings.

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.