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Particulate materials in granular forms are commonly used in industry such as pharmaceutical tablets, energy storage composites, and energetic materials. The microstructures affect product performance during particulate material handling, such as breakage characteristics, and flow permeability. It is essential to establish numerical models to understand complex multi-physics mechanisms in this field. Discrete Element Method (DEM) simulations were commonly used with proper definition of the inter-particle interactions. Recently novel research has been devoted to utilizing machine learning to investigate the effect of granular material microstructures. This paper highlights potential applications of hybrid physics modeling and machine learning approaches for digital design of granular materials. Additionally, potential fabrication techniques especially additive manufacturing are discussed to materialize the digital design of granular materials.
Ge et al. (Thu,) studied this question.