The powder packing behaviour of Fe-based amorphous powders was experimentally monitored and compared with values predicted using the Desmond model and a simulation based on the discrete element method. Powders with different sizes were mixed in various ratios to produce a tri-modal distribution. The simulation results revealed the cohesive force of the powder in terms of the angle of repose and were in agreement with the experimental results. However, the packing behaviour derived from the theoretical model deviated from the experimental results because the interaction among the powder was not considered in the former. Furthermore, the packing fractions for different mixing ratios were investigated through an artificial intelligence framework to determine the optimal mixing ratio. This ratio was validated experimentally and determined to be approximately 8.97% higher than that for the monodisperse large powder case.

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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.

The International Symposium on Innovation in Materials Processing (ISIMP)" was held in Jeju, Korea, from 26th to 29th October 2021. The proceedings for the session on "Integrated Computer-Aided Process Engineering (ICAPE)" were published in October 2022 as a special issue of Materials Transactions (Vol. 63, No. 10). The primary purpose of the ICAPE session was to address the recent advances in scalebridging simulations and characterization to understand, describe, and predict the microstructure-property relationship of newly developed materials in a lab to industrial-level processes. Among the papers presented at the symposium, this article briefly reviews the following topics: macroscale numerical analysis, such as finite element methods (FEM), microstructure simulations such as phase-field modelling (PFM) and molecular dynamics (MD), and optimization techniques such as machine learning (ML) and design of experiments (DOE).