Heterogenous architected materials: enhancing mechanical performance through multi-objective optimization

Computational approaches are a growing necessity for designing complex architected structures, particularly for multifunctional systems with numerous trade-offs. Architected lattice structures formed from repeating unit cells often face multiple loading scenarios in practical applications which make it difficult to determine the best configuration, especially considering emergent behaviors when combining different unit cells. Here, a non-dominated sorting genetic algorithm (NSGA II) is adopted for dual-objective optimization of architected materials for elastic and shear moduli with heterogeneous unit cell placement. Design inputs consist of two different cubic unit cell topologies, each with a fixed unit cell length of 3.6 mm and beam diameter of 0.8 mm. Designs during optimization were evaluated with finite element analysis and search results demonstrated a non-dominated front for elastic and shear modulus trade-offs with a diversity of designs. Computationally optimized heterogenous design solutions increased the elastic modulus limit by 11% and shear modulus limit by 39% when compared to the homogenous and randomized heterogenous solutions. Optimization findings were empirically validated by fabricating 3D printed lattices with mechanical testing of compression and shear moduli. Overall, results provided new strategies for configuring lattice designs that demonstrated advantageous multifunctional performance by optimized heterogenous unit cell placement.