Volume 22, Issue 4 (July 1994)
Radial Basis Function Network Learns Ceramic Processing and Predicts Related Strength and Density
Radial basis function (RBF) neural networks were trained using the data from 273 Si3N4 modulus of rupture (MOR) bars that were tested at room temperature and 135 MOR bars that were tested at 1370°C. Milling time, sintering time, and sintering gas pressure were the processing parameters used as the input features. Flexural strength and density were the outputs by which the RBF networks were assessed. The “nodes at data points” method was used to set the hidden layer centers and output layer training used the gradient descent method. The RBF network predicted strength with an average error of less than 12% and density with an average error of less than 2%. Further, the RBF network demonstrated a potential for optimizing and accelerating the development and processing of emerging ceramic materials.