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

Shuxiao Zhang,Shifeng Guo,Yanhui Zhang,Gaolong Lv,Wei Feng

doi:10.1155/2021/8869928

Abstract

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.

Highlights

Porosity is considered as one of the most important indicators for the characterization of the comprehensive performance of thermal barrier coatings (TBCs)
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. e 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. e 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

Summary

Introduction

Porosity is considered as one of the most important indicators for the characterization of the comprehensive performance of thermal barrier coatings (TBCs). Ye et al [28] implemented a novel hybrid method based on the support vector machine algorithm optimized by the cuckoo search algorithm (CS-SVM) for predicting microstructural features of thermal barrier coatings using various process parameters, such as porosity. Ye et al [29] established a novel approach based on terahertz time-domain spectroscopy combined with principal component analysis support vector machine (PCA-SVM) to characterize microstructural features of thermal barrier coatings such as porosity, poreto-crack ratio, and pore size. Ma et al [31] proposed a hybrid method that combines the BP neural network optimizing Gaussian process regression algorithm and the ultrasonic technique and can be used to characterize porosities of thermal barrier coatings. The above work requires complex signal process methods to extract characteristic parameters for the training network model, which is a time-consuming process

Methods

Results

Conclusion