Mechanical alloying technique is used for production of nanostructured soft magnetic alloys. In this work the back propagation (BP) artificial neural adopted to model the effect of various mechanical alloying parameters i.e. milling time and chemical composition, on the properties of Fe-Ni powders. Lattice parameter, grain size, lattice strain, coersivity and saturation intrinsic flux density are considered as the output of five BP neural networks. The results obtained show the efficiency of designed networks for the prediction of the properties of Fe-Ni powders.

Keywords: Mechanical Alloying, Artificial Neural Network, Coersivity, Saturation Intrinsic Flux Density

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