Predicting mechanical responses of additively manufactured metamaterials with computational efficiency

Abstract

Lattice metamaterials, a class of structures utilized for geometric lightweighting and enhancing structural integrity, feature interconnected elements arranged in specific patterns. These patterns confer unique properties beneficial for improving stiffness, damping, energy absorption, and strength across various applications. However, computational simulation of lattice structures for design and optimization presents significant challenges due to nonlinear and elastic/inelastic effects like instabilities, contacts, rate-dependence, and plasticity. Current methods heavily rely on finite element analysis (FEA), yet they entail high computational complexity and do not fully align with experimental observations. Moreover, additive manufacturing (AM) technologies introduce additional computational complexity due to process-induced anisotropic behaviors. This paper proposes a computational model to construct an effective descriptor by integrating metamaterial topological information with AM conditions. This descriptor serves as a surrogate for FEA models. Additionally, a database is established to correlate the descriptor with experimentally acquired mechanical responses of additively manufactured metamaterials. The bidirectional operation of the envisaged descriptor fulfills two objectives: informing the mechanical response of novel metamaterial models and guiding design and AM processes based on desired outcomes. Model validation demonstrates significant concurrence between predicted and experimental results, evidencing the model's capability to capture inherent nonlinearity in both design and process.