Investigation of Multistage Oxidation Behavior of Al4SiC4 Powders with Aid of Back Propagation Artificial Neural Network

Abstract

In order to investigate the multistage oxidation behavior of aluminum silicon carbide (Al4SiC4), back propagation artificial neural network (BP‐ANN) has been trained and employed considering the oxidation temperature, time, and aspect ratio. The results denote that the BP‐ANN model can accurately and efficiently simulate the oxidation behavior of Al4SiC4 powders with different reaction laws. In addition, the extrapolation ability suggests that the BP‐ANN model can maintain a high accuracy with the coefficient of determination ≥0.801 to expand the experimental data to 1.2 times the original range. By incorporating a real physical picture model developed by the research group, the experimental data can be further expanded to 2.2 times. This study can provide a new path to recognize the oxidation behavior of materials with multistage oxidation.