Using a Graph Neural Network to Learn Mechanical Properties From 3D Lattice Geometry
<p>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 <a href="https://cims.nyu.edu/gcl/papers/panetta2015et.pdf" rel="noopener ugc nofollow" target="_blank">Panetta et al., 2015</a>, 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:</p>
<p><a href="https://medium.com/mesh-consultants/using-a-graph-neural-network-to-learn-mechanical-properties-from-3d-lattice-geometry-b2ef3d39d7b5"><strong>Read More</strong></a></p>