Predicting the grain size and hardness of AZ91/SiC nanocomposite by artificial neural networks

Abstract

In the present study, SiC nanoparticles were added to as-cast AZ91 magnesium alloy through friction stir processing (FSP) and an AZ91/SiC surface nanocomposite layer was produced. A relation between the FSP parameters and grain size and hardness of nanocomposite using artificial neural network (ANN) was established. Experimental results showed that distribution of nanoparticles in the stirred zone (SZ) was not uniform and SZ was divided into two regions. In the ANN modeling, the inputs included traverse speed, rotational speed, and region types. Outputs were hardness and grain size. The model can be used to predict hardness and grain size as functions of rotational and traverse speeds and region types. To check the adequacy of the ANN model, the linear regression analyses were carried out to compute the correlation coefficients. The calculated results were in good agreement with experimental data. Additionally, a sensitivity analysis was conducted to determine the parametric impact on the model outputs.