文献类型：期刊文献

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

作者：Ye, Dongdong[1];Wang, Weize[1];Yin, Changdong[1];Xu, Zhou[1];Fang, Huanjie[1];Huang, Jibo[1];Li, Yuanjun[1]

机构：[1]East China Univ Sci & Technol, Sch Mech & Power Engn, Key Lab Safety Sci Pressurized Syst, Minist Educ, Shanghai 200237, Peoples R China

年份：2020

卷号：10

期号：9

外文期刊名：COATINGS

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000581459700001)】；

基金：This research was funded by National Natural Science Foundation of China (No. 51775189) and Science and Technology Commission of Shanghai Municipality Project (No. 16DZ2260604).

语种：英文

外文关键词：TBCs; terahertz technique; interface delamination; FDTD; SWT-PCA-GA-BP

摘要：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 coefficientR(2)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.