Nondestructive Interface Morphology Characterization of Thermal Barrier Coatings Using Terahertz Time-Domain Spectroscopy

Nondestructive Evaluation of Thermal Barrier Coatings Thickness Using Terahertz Technique Combined with PCA–GA–ELM Algorithm

Thermal barrier coatings (TBCs) are usually used in high temperature and harsh environment, resulting in thinning or even spalling off. Hence, it is vital to detect the thickness of the TBCs. In this study, a hybrid machine learning model combined with terahertz time-domain spectroscopy technology was designed to predict the thickness of TBCs. The terahertz signals were obtained from the samples prepared in laboratory and actual turbine blade. The principal component analysis (PCA) method was used to decrease the data dimensions. Finally, an extreme learning machine (ELM) was proposed to establish the thickness of TBCs prediction model. Genetic algorithm (GA) was selected to optimize the model to make it more accurate. The results showed that the root correlation coefficient (R2) exceeded 0.97 and the errors (root mean square error and mean absolute percentage error) were less than 2.57. This study proposes that terahertz time-domain technology combined with PCA–GA–ELM model is accurate and feasible for evaluating the thickness of the TBCs.

Current Trends in Integration of Nondestructive Testing Methods for Engineered Materials Testing

Material failure may occur in a variety of situations dependent on stress conditions, temperature, and internal or external load conditions. Many of the latest engineered materials combine several material types i.e., metals, carbon, glass, resins, adhesives, heterogeneous and nanomaterials (organic/inorganic) to produce multilayered, multifaceted structures that may fail in ductile, brittle, or both cases. Mechanical testing is a standard and basic component of any design and fabricating process. Mechanical testing also plays a vital role in maintaining cost-effectiveness in innovative advancement and predominance. Destructive tests include tensile testing, chemical analysis, hardness testing, fatigue testing, creep testing, shear testing, impact testing, stress rapture testing, fastener testing, residual stress measurement, and XRD. These tests can damage the molecular arrangement and even the microstructure of engineered materials. Nondestructive testing methods evaluate component/material/object quality without damaging the sample integrity. This review outlines advanced nondestructive techniques and explains predominantly used nondestructive techniques with respect to their applications, limitations, and advantages. The literature was further analyzed regarding experimental developments, data acquisition systems, and technologically upgraded accessory components. Additionally, the various combinations of methods applied for several types of material defects are reported. The ultimate goal of this review paper is to explain advanced nondestructive testing (NDT) techniques/tests, which are comprised of notable research work reporting evolved affordable systems with fast, precise, and repeatable systems with high accuracy for both experimental and data acquisition techniques. Furthermore, these advanced NDT approaches were assessed for their potential implementation at the industrial level for faster, more accurate, and secure operations.

Pulsed terahertz spectroscopy combined with hybrid machine learning approaches for structural health monitoring of multilayer thermal barrier coatings

Structural health monitoring of multilayer thermal barrier coatings (TBCs) is very vital to ensure the structural integrity and service performance of the hot-section components of the aero-engine. In this paper, we theoretically and numerically demonstrated that the terahertz time domain spectrum and the terahertz reflectance spectrum could be adopted to estimate the structure parameters, based on the finite difference time domain (FDTD) algorithm, 64 samples which were imported with three kinds of 64 sets structure parameters had been calculated to obtain the time domain and terahertz reflectance signals. To mimic the actual test signals, the original FDTD simulation signals were processed by adding the Gaussian white noise and wavelet noise reduction. To reduce the data dimension and improve the calculation efficiency during modeling, the principal component analysis (PCA) algorithm was adopted to reduce the dimensions of time-domain data and reflectance data. Finally, these data after multiple signal processing and PCA feature extraction were used to train the extreme learning machine (ELM), combining the genetic algorithm (GA) could optimize the PCA-ELM model and further improve the prediction performance of the hybrid model. Our proposed novel and efficient terahertz nondestructive technology combined with the hybrid machine learning approaches provides great potential applications on the multilayer TBCs structural integrity evaluation.

Terahertz optical thickness and birefringence measurement for thermal barrier coating defect location

We present a normal incidence terahertz reflectivity technique to determine the optical thickness and birefringence of yttria-stabilized zirconia (YSZ) thermal barrier coatings (TBCs). Initial verification of the method was achieved by measurement of a set of fused silica calibration samples with known thicknesses and showed excellent agreement (<1% of refractive index) with the literature. The THz-measured optical thickness and its variation through the depth profile of the YSZ coating are shown to be in good agreement (<4%) with scanning electron microscope cross-sectional thickness measurements. In addition, the position of discontinuities in both the optical thickness and birefringence appear to be correlated to coating failure points observed during accelerated aging trials.

Nondestructive Evaluation of Thermal Barrier Coatings Interface Delamination Using Terahertz Technique Combined with SWT-PCA-GA-BP Algorithm

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.

Prediction of Thermal Barrier Coatings Microstructural Features Based on Support Vector Machine Optimized by Cuckoo Search Algorithm

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.

Thin thermally grown oxide thickness detection in thermal barrier coatings based on SWT-BP neural network algorithm and terahertz technology

Terahertz time-domain spectroscopy is a contactless and nondestructive testing technique that is often used to measure the thickness of layered materials. However, the technique presents limited thickness detection resolution, especially in the thin thermally grown oxide (TGO) of thermal barrier coatings whose thickness is below 30 µm. In this study, an SWT-BP algorithm combining a stationary wavelet transform (SWT) and a backpropagation (BP) neural network was proposed, and the regression coefficient of SWT-detailed results was 0.92. The prediction results were in good agreement with the real-time results; it demonstrated that the proposed algorithm was able to achieve a thickness prediction of up to 1-29 µm of the TGO. The proposed algorithm is suitable for thin thickness detection of the TGO.

Non-Contact, Non-Destructive Testing in Various Industrial Sectors with Terahertz Technology

In this article, we survey various non-contact, non-destructive testing methods by way of terahertz (THz) spectroscopy and imaging designed for use in various industrial sectors. A brief overview of the working principles of THz spectroscopy and imaging is provided, followed by a survey of selected applications from three industries—the building and construction industry, the energy and power industry, and the manufacturing industry. Material characterization, thickness measurement, and defect/corrosion assessment are demonstrated through the examples presented. The article concludes with a discussion of novel spectroscopy and imaging devices and techniques that are expected to accelerate industry adoption of THz systems.

In-situ evaluation of porosity in thermal barrier coatings based on the broadening of terahertz time-domain pulses: simulation and experimental investigations

Porosity is one of the most important indicators for the characterization of the comprehensive performance of thermal barrier coatings (TBCs). Herein, we explored a fast, nondestructive porosity evaluation method based on the terahertz time-domain broadening effect. Different preparation process parameters were used to deposit the ceramic coatings, and the porosity ranged from 9.09% to 21.68%. Monte Carlo simulations were conducted to reveal the transitive relation between porosity and the terahertz time-domain broadening at different extinction coefficients and transmission distances. A transmission mode with an incidence angle of 0° was used to estimate the terahertz dielectric properties of ceramic coatings and the relative broadening ratio of terahertz pulses at different porosities. As a result, the Monte Carlo simulations showed that the time-domain broadening effect was enhanced when the extinction coefficient and transmission distances increased. As the porosity increased, the refractive index decreased and the extinction coefficient increased. The latter was more sensitive to minor porosity changes as demonstrated by linear fitting comparisons. Meanwhile, the relative broadening ratio increased when the porosity increased, and reserved the sensitivity of the extinction coefficient to porosity changes. The effect was more obvious on the relative broadening ratio which experienced multiple transmissions and reflections. Moreover, the relative broadening ratio could be obtained faster and in an easier manner compared to the dielectric parameters in both the transmission and reflection modes, based on single-step tests with the use of actual terahertz wave inspection. Finally, this study proposed a novel, convenient, online, nondestructive, and noncontact porosity evaluation method that could be potentially utilized to evaluate the integrity of TBCs in gas turbine blades.

Contribution of High Mechanical Fatigue to Gas Turbine Blade Lifetime during Steady-State Operation

In this study, the contribution of high thermomechanical fatigue to the gas turbine lifetime during a steady-state operation is evaluated for the first time. An evolution of the roughness on the surface between the thermal barrier coating and bond coating is addressed to elucidate the correlation between operating conditions and the degradation of a gas turbine. Specifically, three factors affecting coating failure are characterized, namely isothermal operation, low-cycle fatigue, and high thermomechanical fatigue, using laboratory experiments and actual service-exposed blades in a power plant. The results indicate that, although isothermal heat exposure during a steady-state operation contributes to creep, it does not contribute to failure caused by coating fatigue. Low-cycle fatigue during a transient operation cannot fully describe the evolution of the roughness between the thermal barrier coating and the bond coating of the gas turbine. High thermomechanical fatigue during a steady-state operation plays a critical role in coating failure because the temperature of hot gas pass components fluctuates up to 140 °C at high operating temperatures. Hence, high thermomechanical fatigue must be accounted for to accurately predict the remaining useful lifetime of a gas turbine because the current method of predicting the remaining useful lifetime only accounts for creep during a steady-state operation and for low-cycle fatigue during a transient operation.