In this investigation, the mechanical and microstructural properties of aluminum composites reinforced by different reinforcing particles including SiC, TiC, ZrO 2 , and B 4 C were optimized using neural network and NSGA-II. In order to obtain the best microstructural and mechanical properties of aluminum composites, different friction stir processing parameters such as rotational and traverse speed and different reinforcing particles type were used in order to fabricate composites. Results show that friction stir processing significantly affect Si particles size as well as dispersion and fraction of reinforcing particles at the stir zone. Moreover, reinforcing particle types influence the mechanical properties of composites due to difference in hardness and thermal expansion of each reinforcement as well as bonding quality between each reinforcement and aluminum matrix. In order to model the correlation between the friction stir processing parameters and microstructural and mechanical properties of the composites, an artificial neural network model was developed. A modified NSGA-II by incorporating diversity preserving mechanism called the " elimination algorithm was employed to obtain the Pareto-optimal set of friction stir processing parameters. Finally, an approach based on TOPSIS method was applied for determining the best compromised solution from the obtained Pareto-optimal set.

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To predict the grain size and microstructure evolution during friction stir welding (FSW) of AZ91 magnesium alloy, a finite element model (FEM) is developed based on the combination of a cellular automaton model and the Kocks  −  Mecking and Laasraoui–Jonas models. First, according to the flow stress curves and using the Kocks  −  Mecking model, the hardening and recovery parameters and the strain rate sensitivity were calculated. Next, an FEM model was established in Deform-3D software to simulate the FSW of AZ91 magnesium alloy. The results of the FEM model are used in microstructure evolution models to predict the grain size and microstructure of the weld zone. There is a good agreement between the simulated and experimental microstructures, and the proposed model can simulate the dynamic recrystallization (DRX) process during FSW of AZ91 alloy. Moreover, microstructural properties of different points in the SZ as well as the effect of the w/v parameter on the grain size and microstructure are considered.

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