Neural network modeling and analysis of gel casting preparation of porous Si3N4 ceramics

Based on orthogonal experimental results of porous Si3N4 ceramics by gel casting preparation, a three-layer back propagation artificial neural network (BP ANN) was developed for predicting the performances of porous Si3N4 ceramics. The results indicated that BP ANN was a very useful and accurate tool for the prediction and optimization of porous Si3N4 ceramics performances. By using the developed ANN model, the influences of the compositions on performances of porous Si3N4 ceramics were investigated, and some important conclusions were drawn as follows: for the flexural strength of Si3N4 ceramics, solid loading has an optimum value where can achieve a maximum value, and the optimum solid loading decreases with the increase of monomer content; the porosity of sintering body monotonically decreases with the increase of solid loading, and it increases with monomer content; the porosity of sintering body monotonically increases with the increase of the ratio of crosslinking agent to monomer.