TiBw/Ti55 复合材料氧化层厚度的支持向量机模型

This study employed the support vector machine (SVM) method combined with the particle swarm optimization (PSO) algorithm to optimize the penalty coefficient C and the radial basis function width coefficient g of the SVM model for predicting the oxide layer thickness of TiBw/Ti55 composites based on oxidation experiments. The optimized penalty coefficient (C) is 64.154 05, and the radial basis function width coefficient (g) is 0.566 89. A predictive model for the oxidation layer thickness of TiBw/Ti55 composites was established. The predicted model has a training set coefficient of determination (R²) of 0.987 06 and a root-mean-square error (RMSE) of 0.054 45. The testing set has a coefficient of determination (R²) of 0.995 04 and a root-mean-square error (RMSE) of 0.132 11. The average error between the predicted and experimental values of the oxide layer thickness of TiBw/Ti55 composites is 5.88%, indicating that the SVM model established in this study has high predictive accuracy. On the basis of the above, the influence of oxidation temperature, volume fraction of the reinforcing phase, and oxidation time on the oxide layer thickness of TiBw/Ti55 composites is analyzed.