Although architected materials based on truss networks have been shown to possess advantageous or extreme mechanical properties, those can be highly affected by tolerances and uncertainties in the manufacturing process, which are usually neglected during the design phase. Deterministic computational tools typically design structures with the assumption of perfect, defect-free architectures, while experiments have confirmed the inevitable presence of imperfections and their possibly detrimental impact on the effective properties. Information about the nature and expected magnitude of geometric defects that emerge from the additive manufacturing processes would allow for new designs that aim to mitigate (or at least account for) the effects of defects and to reduce the uncertainty in the effective properties. To this end, we here investigate the effects of four most commonly found types of geometric imperfections in trusses, applied to eleven representative truss topologies in two and three dimensions. Through our study, we (i) quantify the impact of imperfections on the effective stiffness through computational homogenization, (ii) examine the sensitivity of the various truss topologies with respect to those imperfections, (iii) demonstrate the applicability of the model through experiments on 3D-printed trusses, and (iv) present a machine learning framework to predict the presence of defects in a given truss architecture based merely on its mechanical response.@en

Inspired by crystallography, the periodic assembly of trusses into architected materials has enjoyed popularity for more than a decade and produced countless cellular structures with beneficial mechanical properties. Despite the successful and steady enrichment of the truss design space, the inverse design has remained a challenge: While predicting effective truss properties is now commonplace, efficiently identifying architectures that have homogeneous or spatially varying target properties has remained a roadblock to applications from lightweight structures to biomimetic implants. To overcome this gap, we propose a deep-learning framework, which combines neural networks with enforced physical constraints, to predict truss architectures with fully tailored anisotropic stiffness. Trained on millions of unit cells, it covers an enormous design space of topologically distinct truss lattices and accurately identifies architectures matching previously unseen stiffness responses. We demonstrate the application to patient-specific bone implants matching clinical stiffness data, and we discuss the extension to spatially graded cellular structures with locally optimal properties.@en

Spider's webs are elegant examples of natural composites that can absorb out-of-plane impact energy to capture prey. Different spiders have different methods and structure of webs, and these variations in topologies have a significant effect on the prey catching abilities of the web. Taking inspiration from the spiders, metamaterials that have architectured topology can be fabricated according to end applications such as energy absorbers or impact tolerant materials. In this investigation, we theoretically examined impact loading on various orb-spider webs modeled with metamaterial architecture using materials that show size-dependent behavior. Using the size-dependent properties of nano-reinforced polymer-derived ceramics (PDCs), various metamaterial topologies were evaluated for out-of-plane impact due using ANSYS Ls-Dyna. The material properties capture the size dependency of the ceramics where smaller elements have higher strength due to reduced flaw intensity; the mechanical strength of these elements does not follow the conventional Griffith Theory. In this study, spider web geometries fabricated with PDCs with varying size elements were examined.@en