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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

The powder forging process of die forging, sintering, and upsetting is a convenient way of reducing or eliminating the porosity from traditional powder metallurgy products. Forging of metal powder enhances the demanding high-tensile, impact, and fatigue strength of powder metallurgy products. In this research, a demonstration system has been developed that employs a neural network for advising aluminium—iron composite compositions and optimum process settings with desired properties at an early stage in the design of the component. The input comprises the desired mechanical properties, such as formability index, and the system employs these data as inputs in order to recommend suitable metal powder compositions and process settings such as the particle size, percentage of iron content, preform density, aspect ratio, and compact load. The training data were collected by the experimental set-up in the laboratory for the sintered aluminium—iron composite preforms. Comparison of predicted and experimental data has confirmed the accuracy of the neural network approach; therefore, a new way for recommending suitable metal powder compositions and process settings is explored.