Ultrasonic characterization of thermal barrier coatings porosity through BP neural network optimizing Gaussian process regression algorithm

Zhiyuan Ma; Wei Zhang; Zhongbing Luo; Xu Sun; Zongyi Li; Li Lin

doi:10.1016/j.ultras.2019.105981

Ultrasonic characterization of thermal barrier coatings porosity through BP neural network optimizing Gaussian process regression algorithm

Ultrasonics. 2020 Jan:100:105981. doi: 10.1016/j.ultras.2019.105981. Epub 2019 Aug 16.

Authors

Zhiyuan Ma¹, Wei Zhang¹, Zhongbing Luo¹, Xu Sun¹, Zongyi Li¹, Li Lin²

Affiliations

¹ NDT & E Laboratory, Dalian University of Technology, Dalian 116024, China.
² NDT & E Laboratory, Dalian University of Technology, Dalian 116024, China. Electronic address: linli@dlut.edu.cn.

PMID: 31479965
DOI: 10.1016/j.ultras.2019.105981

Abstract

Porosity is an integral part of thermal barrier coatings (TBCs) and is required to provide thermal insulation and to accommodate operational thermal stresses. Accurate characterization of the TBCs porosity is difficult due to the complex pore morphology and ultra-thin coating thickness. In this paper, a BP neural network optimizing Gaussian process regression (GPR) algorithm, termed BP-GPR, is presented to characterize the TBCs porosity based on a constructed ultrasonic reflection coefficient amplitude spectrum (URCAS). The characteristic parameters of URCAS are optimized through the BP neural network combined with a high determination coefficient R² rule. Then the optimized parameters are utilized to train the GPR algorithm for predicting the unknown TBCs porosity. The proposed BP-GPR method was demonstrated through a series of finite element method (FEM) simulations, which were implemented on random pore models (RPMs) of plasma spraying ZrO₂ coating with a thickness of 300 μm and porosities of 1%, 3%, 5%, 7%, and 9%. Simulation results indicated the relative errors of the predicted porosity of RPMs were 6.37%, 7.62%, 1.07%, and 1.07%, respectively, which has 32% and 48% accuracy higher than that predicted only by BP neural network or GPR algorithm. It is verified that the proposed BP-GPR method can accurately characterize the porosity of TBCs with complex pore morphology.

Keywords: BP neural network; Gaussian process regression; Porosity; Thermal barrier coating; Ultrasonic.