Lattice structures are known to have high performance-to-weight
ratios because of their highly efficient material distribution in
a given volume. However, their inherently large void fraction leads
to low mechanical properties compared to the base material, high anisotropy,
and brittleness. Most works to date have focused on modifying the
spatial arrangement of beam elements to overcome these limitations,
but only simple beam geometries are adopted due to the infinitely
large design space associated with probing and varying beam shapes.
Herein, we present an approach to enhance the elastic modulus, strength,
and toughness of lattice structures with minimal tradeoffs by optimizing
the shape of beam elements for a suite of lattice structures. A generative
deep learning-based approach is employed, which leverages the fast
inference of neural networks to accelerate the optimization process.
Our optimized lattice structures possess superior stiffness (+59%),
strength (+49%), toughness (+106%), and isotropy (+645%) compared
to benchmark lattices consisting of cylindrical beams. We fabricate
our lattice designs using additive manufacturing to validate the optimization
approach; experimental and simulation results show good agreement.
Remarkable improvement in mechanical properties is shown to be the
effect of distributed stress fields and deformation modes subject
to beam shape and lattice type.