Prediction of the sintering shrinkage of glass-alumina functionally graded materials by a BP artificial neural network

The shrinkage of the glass-alumina functionally graded materials (G-A FGMs) as a function of sintering temperature, layers, and the alumina content was predicted by a back propagation artificial neural network (BP-ANN). The BP-ANN was composed of an input layer, a hidden layer, and an output layer. 21 sets of experimental data were trained, in which the temperature, layers, and the alumina content as input parameters whereas the shrinkage as the output parameter. 5 sets of experimental data were used to identify the accuracy of the BP-ANN. From the prediction, selection of the hidden layer neurons is essential for the convergence of the BP-ANN. The minimum predicted errors less than 6.6% are obtained with 8 neurons. Comparison of the predicted shrinkage shows that the increase of layers or alumina content is beneficial to the increase of the shrinkage and expansion resistance for the G-A FGMs.