Neural networks (NNs) are employed to study the deformation characteristics of sintered aluminium preforms. The proposed NN model has used the measured parameters, namely the load, the aspect ratio and the initial preform fractional density ratio to predict multiple material characteristics, namely the axial stress, the hoop stress, the hydrostatic stress, the axial strain, the hoop strain and the Poisson's ratio. The model is based on a 'four layered NN' with back propagation learning algorithm. The experimental set-up available in the laboratory has been used to get the training data for the sintered aluminium with various preform densities and different aspect ratios (0.50, 0.75 and 1.00) using MoS 2 as lubricant. The predicted values from the proposed NN coincide well with the experimental values. In addition, a comparative study between the regression analysis and the NN revealed that the NN can predict the material characteristics of sintered aluminium preform better than regression polynomials within a few per cent error. Nomenclature

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A neural network model has been developed for the prediction of strain hardening and densification constants of sintered aluminium preforms. The model is based on a three layer neural network with a back propagation learning algorithm. The training data were collected by the experimental setup in the laboratory for sintered aluminium and with various preform densities with different aspect ratios by using MoS 2 as a lubricant. The network is trained to predict the values of strain hardening exponent index n i , strength coefficient k i , density power law exponent B i and density constant C i . Regression analysis between experimental and values predicted by the neural network shows the least error. This approach helps in the reduction of the experimentation required to determine these constants.PM/1097

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