Performances of three models in predicting packing densities and optimal mixing fractions of mixtures of micropowders with different sizes

Mixing powders with different sizes is a common method to tune the packing density. For the first time, pre-dicted packing density and optimal mixing fraction from different linear packing models (de Larrard's, Kwan's, and Yu's) are compared against the experimental results of mixtures of micropowders (with different particle sizes: 2,10, and 70 pm). Regarding the predicted packing density, Kwan's model achieved the smallest prediction deviations for three mixing systems of 10 pm and 2 pm powders, 70 pm and 10 pm powders, and 70 pm, 10 pm, and 2 pm powders, while de Larrard's model achieved the smallest prediction deviation for the mixing system of 70 pm and 2 pm powders. Overall for the predicted packing density, Kwan's model achieved the best prediction performance with the lowest average mean absolute error of 2.2%. Regarding the predicted optimal mixing frac-tion, Kwan's model outperformed the other two models for the mixing system of 10 pm and 2 pm powders and the mixing system of 70 pm and 2 pm powders, while Yu's model outperformed the other two models for the mixing system of 70 pm and 10 pm powders. Possible reasons of the better performances of Kwan's model in both prediction aspects include the consideration of wedging effect.(c) 2021 Elsevier B.V. All rights reserved.

Automated summary

For the first time, different linear packing models (de Larrard's, Kwan's, and Yu's) were compared for their ability to predict packing density and optimal mixing fraction of mixtures of micropowders. The results showed that Kwan's model outperformed the other two models in predicting both packing density and optimal mixing fraction.