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.