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论文类型：期刊论文

发表时间：2020-07-01

发表刊物：NDT & E INTERNATIONAL

卷号：113

页面范围：102272

摘要：Accurate porosity characterization is of great significance to evaluate abradable seal coating comprehensive performance. In this paper, a support vector regression model optimized by particle swarm optimization algorithm, termed PSO-SVR, is proposed to predict coating porosity based on multi-scale ultrasonic attenuation coefficient. To decouple the 'multi-scale scattering effect' of ultrasonic propagation in abradable seal coating, the echo signals are decomposed using Continuous Wavelet Transform (CWT). The ultrasonic responses in different frequency bands could be sufficiently extracted through the multi-scale ultrasonic attenuation coefficient obtained by CWT. Subsequently, taking the coefficients as input vectors, the SVR model is established. The parameters of SVR, including the penalty factor C, kernel function parameter gamma, and insensitive loss epsilon, are optimized through PSO algorithm. Finally, the optimized SVR model is applied to predict the porosity of the AlSi-polyester abradable seal coating prepared by plasma spraying. The normalized mean squared error MSE of the validation set is 0.067 with a determination coefficient R-2 of 0.947. The prediction results show that the PSO-SVR model has higher accuracy, better generalization ability, and stronger robustness in the case with limited experimental data, compared with classical Artificial Neural Network (ANN) models, e.g. Back Propagation (BP), Radial Basis Function (RBF), and General Regression Neural Network (GRNN).