Modeling and design of heterogeneous hierarchical bioinspired spider web structures using deep learning and additive manufacturing.

Spider webs are incredible biological structures, comprising thin but strong silk filament and arranged into complex hierarchical architectures with striking mechanical properties (e.g., lightweight but high strength, achieving diverse mechanical responses). While simple 2D orb webs can easily be mimicked, the modeling and synthesis of 3D-based web structures remain challenging, partly due to the rich set of design features. Here, we provide a detailed analysis of the heterogeneous graph structures of spider webs and use deep learning as a way to model and then synthesize artificial, bioinspired 3D web structures. The generative models are conditioned based on key geometric parameters (including average edge length, number of nodes, average node degree, and others). To identify graph construction principles, we use inductive representation sampling of large experimentally determined spider web graphs, to yield a dataset that is used to train three conditional generative models: 1) an analog diffusion model inspired by nonequilibrium thermodynamics, with sparse neighbor representation; 2) a discrete diffusion model with full neighbor representation; and 3) an autoregressive transformer architecture with full neighbor representation. All three models are scalable, produce complex, de novo bioinspired spider web mimics, and successfully construct graphs that meet the design objectives. We further propose an algorithm that assembles web samples produced by the generative models into larger-scale structures based on a series of geometric design targets, including helical and parametric shapes, mimicking, and extending natural design principles toward integration with diverging engineering objectives. Several webs are manufactured using 3D printing and tested to assess mechanical properties.

Sequential Multimaterial Additive Manufacturing of Functionally Graded Biopolymer Composites.

Cellulose, chitin, and pectin are three of the most abundant natural materials on Earth. Despite this, large-scale additive manufacturing with these biopolymers is used only in limited applications and frequently relies on extensive refinement processes or plastic additives. We present novel developments in a digital fabrication and design approach for multimaterial three-dimensional printing of biopolymers. Specifically, our computational and digital fabrication workflow-sequential multimaterial additive manufacturing-enables the construction of biopolymer composites with continuously graded transitional zones using only a single extruder. We apply this method to fabricate structures on length scales ranging from millimeters to meters. Transitional regions between materials created using these methods demonstrated comparable mechanical properties with homogenous mixtures of the same composition. We present a computational workflow and physical system support a novel and flexible form of multimaterial additive manufacturing with a diverse array of potential applications.