Abstract
It is known that the interaction between suspended ceramic nanoparticles (TiB2 and TiO2) in molten alloys affects the strengthening mechanisms of nanoparticle reinforced composites. The present study follows a comparative approach to investigate this phenomenon during casting process of aluminum composites reinforced by TiB2 and TiO2 nanoparticles. Microstructural studies accompanied by the measurements of hardness and tensile strength showed that the highest improvement in mechanical properties of the nanocomposites was achieved when Orowan, load bearing mechanism and Hall-Petch mechanisms were simultaneously engaged in the strengthening process of the metal matrix. In order to predict the mechanical properties, four artificial neural networks based on multi-input and multi-output (MIMO) and single-input and multi-output (SIMO) models were created using Bayesian regularization, and cross validated. Showing errors less than 5%, the developed models can reliably be used to reduce the product development time and fabrication of the aluminum matrix nanocomposites in future under different processing conditions.
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