Journal of Powder Materials (한국분말재료학회지)

The Korean Powder Metallurgy & Materials Institute (한국분말재료학회 (*구 분말야금학회))
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Modeling the Relationship between Process Parameters and Bulk Density of Barium Titanates

  • Park, Sang Eun (Virtual Materials Lab, School of Materials Science and Engineering, Engineering Research Institute, Gyeongsang National University) ;
  • Kim, Hong In (Virtual Materials Lab, School of Materials Science and Engineering, Engineering Research Institute, Gyeongsang National University) ;
  • Kim, Jeoung Han (Department of Materials Science & Engineering, Hanbat National University) ;
  • Reddy, N.S. (Virtual Materials Lab, School of Materials Science and Engineering, Engineering Research Institute, Gyeongsang National University)
  • Received : 2019.08.08
  • Accepted : 2019.09.05
  • Published : 2019.10.28
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Abstract

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.

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