Tuning mechanical response of nonuniform triangular lattice material via graph neural network based inverse design algorithm

Strut-based lattice materials with uniform topology have recently received many attentions from scientific and industrial communities because of its exceptional mechanical properties-e.g., high strength-to-weight ratio and energy-absorption-to-weight ratio-and ease of manufacturing. However, many natural strut-based lattice materials show highly nonuniform topology and exhibit much larger design space in terms of linear and nonlinear mechanical behaviors. The exploration of this vast design space is very challenging. Recently, owing to the emergence of deep learning (DL) neural networks, DL-based approach offers great potential to tackle traditional challenges. In this preliminary study, an integrated numerical-DL approach has been developed to tuning the mechanical response of nonuniform strut-based materials. A nonuniform triangular topology was selected to construct strut-based lattice material as an example. Linear elastic material model was employed for each strut of lattice materials. Graph neural network was chosen to build surrogate model to predict global mechanical response-particularly nondimensional effective stiffness, nondimensional effective critical strength, and effective Poisson's ratio-of lattice material. Genetic algorithm was exploited to inversely design nonuniform strut-based lattice materials with optimized properties. The nondimensional effective stiffness, nondimensional effective critical strength, effective Poisson's ratio can be easily tuned in a wide range. Our results demonstrated the feasibility of our integrated approach to design highly nonuniform strut-based lattice materials.