Abstract-Characterization of thermal barrier coatings microstructural features using terahertz spectroscopy

Dongdong Ye, Weize Wang, Haiting Zhou, Huanjie Fang, Jibo Huang, Yuanjun Li, Hanhong Gong, Zhen Li,



https://www.sciencedirect.com/science/article/abs/pii/S0257897220305053

A novel approach was presented to characterize microstructural features of thermal barrier coatings (TBCs) using terahertz spectroscopy based on machine learning algorithms. In this study, the microstructures of yttria-stabilized zirconia (YSZ) atmospheric-plasma-sprayed (APS) thermal barrier coatings were regulated by choosing different kinds of spray powders, distances and power during processing. A terahertz time-domain spectroscopy system configurated transmission mode with an incidence angle of 0° was employed to estimate terahertz properties of porous YSZ ceramic coatings, including refractive index, extinction coefficient and relative time-domain broadening ratio. The variation tendency of terahertz properties of YSZ ceramic coatings with different microstructure features (porosity, pore to crack ratio, pore size) were investigated. Principal component analysis (PCA) method was adopted to reduce the dimensions of refractive index and extinction coefficient spectra data at the range of 0.6–1.4 THz and to ensure that different terahertz properties could be treated as inputs with similar weights during modeling. Three models (multiple linear regression (MLR), back-propagation (BP) neural network and support vector machine (SVM)) were set up to conduct regression analysis. As a result, according to the contribution rates of eigenvectors, the top one principal component of refractive index spectra data and the top two principal components of extinction coefficient spectra data were selected as the model inputs. The correlation coefficient comparisons showed that the characterization accuracy of PCA-SVM reached by over 95% and outperformed the other models. Finally, this study proposed that THz nondestructive technology combined with machine learning technique is efficient and feasible for microstructural features characterization and has profound implications for the structure integrity of TBCs evaluation in gas turbine blades.

Abstract-Topcoat Thickness Measurement of Thermal Barrier Coating of Gas Turbine Blade Using Terahertz Wave

1. Tetsuo Fukuchi¹, 
2. Norikazu Fuse¹,
3. Mitsutoshi Okada², 
4. Takayuki Ozeki²,
5. Tomoharu Fujii², 
6. Maya Mizuno³ and
7. Kaori Fukunaga³

Article first published online: 19 JUN 2014

DOI: 10.1002/eej.22624

© 2014 Wiley Periodicals, Inc.

http://onlinelibrary.wiley.com/doi/10.1002/eej.22624/abstract

The topcoat thickness of thermal barrier coating (TBC) applied to a gas turbine blade was measured using terahertz waves. The refractive index of the topcoat was obtained by frequency analysis of the waves reflected from the topcoat surface and the interface between the topcoat and bondcoat. The surface roughness of the topcoat surface was considered for improving the accuracy of the refractive index calculation. The topcoat thickness was obtained from the refractive index and the time separation between the reflected waves. The validity of the method was confirmed using a TBC sample with variable topcoat thickness, and the obtained topcoat thickness was in agreement with measurement results using a contact thickness gauge. Error analysis showed that the measurement error of the topcoat thickness was about 3%. The method was applied to a gas turbine blade with TBC, and the measured topcoat thickness agreed with microscope observation results of the cross section to within 6%, which was within the range of measurement error. The effect of curvature of the gas turbine blade surface did not result in significant measurement error. The results showed that terahertz waves are effective for nondestructive measurement of the topcoat thickness of TBC applied to gas turbine blades.