Dynamic Corrosion Prediction of Alloy Cast Iron Based on RBF Neural Network

The sample data were measured by the dynamic mass loss method. The RBF neural network prediction model of alloy cast iron corrosion rate was established by the toolbox function of Matlab, and the prediction precision of network model was studied. The results show that under this sample set and training condition, RBF neural network model reflected the non-linear relationship between corrosion time and main components of alloy cast iron and corrosion rate, and it was used to predict the dynamic corrosive nature of alloy cast iron in high temperature concentrated alkaline solution. When the spread coefficient of RBF neural network was 0.47, the error between measured values of dynamic corrosion rate and predicted values of network was minimum, and the appraisal accuracy rate of corrosion resistance reached 100%.