Fast stress evaluation of the top coat of thermal barrier coatings under CaO–MgO–Al2O3–SiO2 penetration based on image recognition and an artificial neural network

2021 ◽  
Author(s):  
Luyuan Ning ◽  
Zhenwei Cai ◽  
Xiaofeng Zhao ◽  
Yingzheng Liu ◽  
Weizhe Wang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Thermal Barrier Coatings ◽  
Image Recognition ◽  
Thermal Barrier ◽  
Barrier Coatings ◽  
Stress Evaluation ◽  
Artificial Neural

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

Coatings ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 859
Author(s):  
Dongdong Ye ◽  
Weize Wang ◽  
Changdong Yin ◽  
Zhou Xu ◽  
Huanjie Fang ◽  
...  
Keyword(s):  
Neural Network ◽  
Thermal Barrier Coatings ◽  
Time Domain ◽  
Evaluation Method ◽  
Structural Integrity ◽  
Early Stage ◽  
Percentage Error ◽  
Thermal Barrier ◽  
Barrier Coatings ◽  
Interface Delamination

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.

Hyperspectral Image Recognition Based on Artificial Neural Network

2018 ◽  
Vol 16 (5) ◽  
Author(s):  
Feng Liang ◽  
Hanhu Liu ◽  
Xiao Wang ◽  
Yanyan Liu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Image Recognition ◽  
Hyperspectral Image ◽  
Artificial Neural

scholarly journals Porosity Characterization of Thermal Barrier Coatings by Ultrasound with Genetic Algorithm Backpropagation Neural Network

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Shuxiao Zhang ◽  
Gaolong Lv ◽  
Shifeng Guo ◽  
Yanhui Zhang ◽  
Wei Feng
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Thermal Barrier Coatings ◽  
Amplitude Spectrum ◽  
Physical Models ◽  
Average Error ◽  
Thermal Barrier ◽  
Barrier Coatings ◽  
The Time Domain

Porosity is considered as one of the most important indicators for the characterization of the comprehensive performance of thermal barrier coatings (TBCs). In this study, the ultrasonic technique and the artificial neural network optimized with the genetic algorithm (GA_BPNN) are combined to develop an intelligent method for automatic detection and accurate prediction of TBCs’s porosity. A series of physical models of plasma-sprayed ZrO2 coating are established with a thickness of 288 μm and porosity varying from 5.71% to 26.59%, and the ultrasonic reflection coefficient amplitude spectrum (URCAS) is constructed based on the time-domain numerical simulation signal. The characteristic features f 1 , f 2 , A max , Δ A of the URCAS, which are highly dependent on porosity, are extracted as input data to train the GA_BPNN model for predicting the unknown porosity. The average error of the prediction results is 1.45%, which suggests that the proposed method can achieve accurate detection and quantitative characterization of the porosity of TBCs with complex pore morphology.

Sign in / Sign up

Export Citation Format

Share Document