NDT & E Laboratory, Dalian University of Technology, Dalian 116024, China.
NDT & E Laboratory, Dalian University of Technology, Dalian 116024, China.
Ultrasonics. 2020 Jan;100:105981. doi: 10.1016/j.ultras.2019.105981. Epub 2019 Aug 16.
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 R 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 ZrO 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.
孔隙度是热障涂层（TBCs）的一个组成部分，需要提供隔热并适应运行热应力。由于复杂的孔形态和超薄的涂层厚度，准确表征 TBCs 的孔隙度是困难的。在本文中，提出了一种基于构建的超声反射系数幅度谱（URCAS）的 BP 神经网络优化高斯过程回归（GPR）算法，称为 BP-GPR，用于表征 TBCs 的孔隙度。通过结合高决定系数 R 规则的 BP 神经网络对 URCAS 的特征参数进行优化。然后，利用优化后的参数对 GPR 算法进行训练，以预测未知的 TBCs 孔隙度。通过一系列有限元方法（FEM）模拟验证了所提出的 BP-GPR 方法，这些模拟是在厚度为 300μm 且孔隙率分别为 1%、3%、5%、7%和 9%的等离子喷涂 ZrO 涂层的随机孔模型（RPM）上进行的。模拟结果表明，预测 RPMs 孔隙度的相对误差分别为 6.37%、7.62%、1.07%和 1.07%，其预测精度比仅使用 BP 神经网络或 GPR 算法分别提高了 32%和 48%。验证了所提出的 BP-GPR 方法可以准确地表征具有复杂孔形态的 TBCs 的孔隙度。
