| description abstract | Architected materials have received increasing attention due to their exotic mechanical properties including ultra-high stiffness-to-weight ratio, strength, energy absorption, and toughness. Typically, their mechanical properties and deformation behavior arise from the periodically tessellated unit cells. Although periodicity in conventional architected materials promises homogeneity and predictability in mechanical behaviors, it imposes a strong restriction on the design space of architected materials. Inspired by biomaterials, aperiodic and disordered designs significantly expand the design space and have been proven effective in controlling and optimizing linear elastic properties. Taking a step further, here we focus on the nonlinear properties of irregular lattice materials under large deformation, including the stress–strain curve and specific energy absorption. Such materials are generated by a nature-inspired virtual growth program that assembles predefined geometric building blocks in a stochastic yet controllable manner. The nonlinear properties are analyzed through quasi-static compression experiments and large-scale numerical simulations. Based on the well-agreed experimental and numerical results, through the lens of machine learning techniques, the nonlinear properties show a strong correlation with the appearance frequency of the building blocks and their local connectivity, regardless of the nondeterministic nature of the microstructures. A practical constitutive model is proposed for future developments such as generative design and engineering application. Our research offers valuable insights and serves as an inspiration for deeper exploration into the intricate structure–property relationships within materials with aperiodic and disordered microstructures. | |
