Optimization of Densification Behavior of a Soft Magnetic Powder by Discrete Element Method and Machine Learning

Densification of amorphous powder is crucial for preventing magnetic dilution in energy-conversion parts owing to its low coercivity, high permeability, and low core loss. As it cannot be plastically deformed, its packing fraction is controlled by optimizing the particle size and morphology. This study proposes a method for enhancing the densification of an amorphous powder after compaction, achieved by mixing three types of powders of different sizes. Powder packing behavior for various powder mixing combinations is predicted by an analytical model (i.e., Desmond's model) and a computational simulation based on the discrete element method (DEM). The DEM simulation predicts the powder packing behavior more accurately than the Desmond model because it incorporates the cohesive and van der Waals forces. Finally, a machine learning model is created based on the data collected from the DEM simulation, which achieves a packing fraction of 94.14% and an R-squared value for the fit of 0.96.