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

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 develo...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Complexity (New York, N.Y.) Ročník 2021; číslo 1
Hlavní autoři: Zhang, Shuxiao, Lv, Gaolong, Guo, Shifeng, Zhang, Yanhui, Feng, Wei
Médium: Journal Article
Jazyk:angličtina
Vydáno: Hoboken Hindawi 2021
John Wiley & Sons, Inc
Wiley
Témata:
Algorithms
Analysis
Artificial intelligence
Artificial neural networks
Back propagation
Back propagation networks
Coatings
Genetic algorithms
Machine learning
Mathematical models
Morphology
Neural networks
Nondestructive testing
Numerical analysis
Porosity
Porous materials
Predictions
Principal components analysis
Reflectance
Simulation methods
Support vector machines
Thermal barrier coatings
Thermodynamic properties
Ultrasonic methods
Velocity
Wavelet transforms
Zirconium dioxide
China
ISSN:1076-2787, 1099-0526
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí: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 f1,f2,Amax,ΔA 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.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1076-2787
1099-0526
DOI:10.1155/2021/8869928