Apparent viscosity prediction of alumina–paraffin suspensions using artificial neural networks

A neural network model has been developed for the prediction of apparent viscosity of alumina–paraffin suspensions used in low-pressure injection moulding (LPIM) process. The model is based on a three-layer neural network with a backpropagation-learning algorithm. The training data were collected by the rotational viscometry followed by a nonlinear regression. The network is trained to predict the values of power-law model parameters suitable to describe non-Newtonian fluids. A comparison between experimental values and those predicted by the neural network shows a good coincidence. The approach helps to reduce the amount of experiments required to determine these constants in practice.