文献类型：期刊文献

英文题名：Characterization of thermal barrier coatings microstructural features using terahertz spectroscopy

作者：Ye, Dongdong[1];Wang, Weize[1];Zhou, Haiting[2];Fang, Huanjie[1];Huang, Jibo[1];Li, Yuanjun[1];Gong, Hanhong[3,4];Li, Zhen[3,4]

机构：[1]East China Univ Sci & Technol, Sch Mech & Power Engn, Key Lab Pressure Syst & Safety, Minist Educ, 130 Meilong Rd, Shanghai 200237, Peoples R China;[2]China Jiliang Univ, Dept Qual & Safety Engn, 258 Xueyuan St, Hangzhou 310018, Peoples R China;[3]Univ Shanghai Sci & Technol, Shanghai Key Lab Modern Opt Syst, Minist Educ, 516 Jungong Rd, Shanghai 200093, Peoples R China;[4]Univ Shanghai Sci & Technol, Engn Res Ctr Opt Instrument & Syst, Minist Educ, 516 Jungong Rd, Shanghai 200093, Peoples R China

年份：2020

卷号：394

外文期刊名：SURFACE & COATINGS TECHNOLOGY

收录：;EI(收录号：20202208713369);WOS:【SCI-EXPANDED(收录号:WOS:000542100500025)】；

基金：This research is sponsored by the National Natural Science Foundation of China (Grant No. 51775189), Science and Technology Commission of Shanghai Municipality Project (16DZ2260604). We thank the support from the Shanghai Key Laboratory of Modern Optical Systems and University of Shanghai for Science and Technology.

语种：英文

外文关键词：Thermal barrier coatings; APS; Microstructural features; Terahertz; Machine learning

摘要：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 degrees 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.