Using a Graph Neural Network to Learn Mechanical Properties From 3D Lattice Geometry

Additive manufacturing is a promising method for developing metamaterials: while the material (resin, polymer etc.) printed by the machine is typically of one type (with a predetermined stiffness etc.), we can achieve varying properties and compressive behaviours by changing the geometry of the print. Symmetric lattices are particularly appealing from a design perspective, and it is possible to attain a huge spectrum of material behaviour through a variation of the underlying geometry (one of my favourite papers on this topic is Panetta et al., 2015, which we draw on to generate our lattice data). However, the material properties of these lattices are typically assessed through a finite element simulation, which can be costly and time-consuming. The question motivating our recent research was:

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Tags: Graph Neural