An artificial neural network (ANN) model is developed for the analysis and simulation of correlation between flake powder metallurgy parameters and properties of AA2024-SiC nanocomposites. The input parameters of the model are AA 2024 matrix size, ball milling time, and weight percentage of SiC nanoparticles and the output parameters are density and hardness. The model can predict the density and hardness of the unseen test data with a correlation of 0.986 beyond the experimental data. A user interface is designed to predict properties at new instances. We have used the model to simulate the individual as well as the combined influence of parameters on the properties. Moreover, we have analyzed the calculated results from the powder metallurgical point of view. The developed model can be used as a guide for further composite development.

Abstract
1. Introduction
2. Materials and Methods
    2.1 Experimental data
    2.2 ANN Methodology
3. Results and Discussion
    3.1 Optimization of ANN model hyperparameters.
    3.2 Performance of the developed model
    3.3 Sensitivity analysis
    3.4 Finding the index of relative importance.
4. Conclusions
References

• A-Hyun Jeon(Virtual Materials Lab, Department of Metallic & Materials Engineering, School of Materials Science and Engineering, Engineering Research Institute, Gyeongsang National University)
• Hong In Kim(Virtual Materials Lab, Department of Metallic & Materials Engineering, School of Materials Science and Engineering, Engineering Research Institute, Gyeongsang National University)
• Hyokyung Sung(Virtual Materials Lab, Department of Metallic & Materials Engineering, School of Materials Science and Engineering, Engineering Research Institute, Gyeongsang National University)
• N. S. Reddy(Virtual Materials Lab, Department of Metallic & Materials Engineering, School of Materials Science and Engineering, Engineering Research Institute, Gyeongsang National University) Corresponding Author