Corrosion Depth Prediction of Alloy Cast Iron Based on BP Neural Network

The sample data of BP neural network were measured by the dynamic hydrometer method. The 4×15×8×1 BP neural network model was established by the toolbox function of Matlab, and the prediction precision and application of network model were studied. The results showed that under this sample set and training condition, 4×15×8×1 BP neural network model reflected the non-linear relationship between corrosion time and main components of alloy cast iron and corrosion depth very well, and it was used to predict dynamic corrosive nature of alloy cast iron in high temperature concentrated alkaline solution. When rare earth and copper contents were relatively low and proper, the caustic corrosion resistance function of rare earth and copper was comparatively obvious, the higher the nickel content, the obvious the caustic corrosion resistance function.