The properties of powder metallurgy products are related to their densities. In the present work, we demonstrate a method to apply artificial neural networks (ANNs) trained on experimental data to predict the bulk density of barium titanates. The density is modeled as a function of pressure, press rate, heating rate, sintering temperature, and soaking time using the ANN method. The model predictions with the training and testing data result in a high coefficient of correlation (R2 = 0.95 and Pearson’s r = 0.97) and low average error. Moreover, a graphical user interface for the model is developed on the basis of the transformed weights of the optimally trained model. It facilitates the prediction of an infinite combination of process parameters with reasonable accuracy. Sensitivity analysis performed on the ANN model aids the identification of the impact of process parameters on the density of barium titanates.

Abstract
1. Introduction
2. Materials and Methods
3. Results and discussion
    3.1 Transformation of Artificial Neural NetworksWeights
4. Conclusions
References

• Sang Eun Park(Virtual Materials Lab, School of Materials Science and Engineering, Engineering Research Institute, Gyeongsang National University)
• Hong In Kim(Virtual Materials Lab, School of Materials Science and Engineering, Engineering Research Institute, Gyeongsang National University)
• Jeoung Han Kim(Department of Materials Science & Engineering, Hanbat National University)
• N. S. Reddy(Virtual Materials Lab, School of Materials Science and Engineering, Engineering Research Institute, Gyeongsang National University) Corresponding Author