Journal
JOURNAL OF MATERIALS PROCESSING TECHNOLOGY
Volume 203, Issue 1-3, Pages 208-215Publisher
ELSEVIER SCIENCE SA
DOI: 10.1016/j.jmatprotec.2007.09.058
Keywords
low-pressure injection moulding; ceramics; viscosity prediction; artificial neural networks
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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. (C) 2007 Published by Elsevier B.V.
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