Analysis of cold compaction for Fe-C, Fe-C-Cu powder design based on constitutive relation and artificial neural networks

Abstract

The constitutive relations for Fe-C and Fe-C-Cu powder compactions were investigated with the three consitituents: i) powder design parameters, ii) material related properties, and iii) final compaction properties. With the concept of materials informatics, this approach enables to predict the final compaction properties depending on the material conditions. The correlations between powder design parameters (particle size, graphite content, lubricant content, particle size distribution, copper content) and material related properties (rho(Tap), gamma, a, b, n) in Shima-Oyane model were characterized by the compaction experiments and artificial neural network (ANN) model. The ANN model was developed to predict the effect of powder design parameters on the material related properties. The average mean absolute percentage error of predicted material related properties was 2.194%. The final properties (green density, density gradient, effective stress, hydrostatic stress, effective strain, volumetric strain) were calculated by the compaction simulation based on the experimental and predicted material related properties.