Search

Abstract Grayscale digital light processing (DLP) printing is a simple yet effective way to realize the variation of material properties by tuning the grayscale value. However, there is a lack of available design methods for grayscale DLP 3D‐printed structures due to the complexities arising from the voxel‐level grayscale distribution, nonlinear material properties, and intricate structures. Inspired by the dexterous motions of natural organisms, a design and fabrication framework for grayscale DLP‐printed soft robots is developed by combining a grayscale‐dependent hyperelastic constitutive model and a voxel‐based finite‐element model. The constitutive model establishes the relationship between the projected grayscale value and the nonlinear mechanical properties, while the voxel‐based finite‐element model enables fast and efficient calculation of the mechanical performances with arbitrarily distributed material properties. A multiphysics modeling and experimental method is developed to validate the homogenization assumption of the degree of conversion (DoC) variation in a single voxel. The design framework is used to design structures with reduced stress concentration and programmable multimodal motions. This work paves the way for integrated design and fabrication of functional structures using grayscale DLP 3D printing.

Abstract Architected materials such as lattices are capable of demonstrating extraordinary mechanical performance. Lattices are often used for their stretch-dominated behavior, which gives them a high degree of stiffness at low-volume fractions. At the other end of the stiffness spectrum, bending-dominated lattices tend to be more compliant and are of interest for their energy absorption performance. Here, we report a class of ultra-compliant interwoven lattices that demonstrate up to an order of magnitude improvement in compliance over their traditional counterparts at similar volume fractions. This is achieved by selectively decoupling nodes and interweaving struts in bending-dominated lattices, inspired by observations of this structural principle in the lattice-like arrangement of the Venus flower basket sea sponge. By decoupling nodes in this manner, we demonstrate a simple and near-universal design strategy for modulating stiffness in lattice structures and achieve among the most compliant lattices reported in the literature.

Lattice-type periodic metamaterials with beam-like struts have been extensively investigated in recent years thanks to the progress in additive manufacturing technologies. However, when lattice structures are subject to large deformations, computational simulation for design and optimization remains a major challenge due to complex nonlinear and inelastic effects, such as instabilities, contacts, rate-dependence, plasticity, or damage. In this contribution, we demonstrate for the first time the efficient and accurate computational simulation of beam lattices using a finite deformation 3D beam formulation with inelastic material behavior, instability analysis, and contacts. In particular, the constitutive model captures elasto-visco-plasticity with damage/softening from the Mullins effect. Thus, the formulation can be applied to the modeling of both stiffer metallic and more flexible polymeric materials. The approach is demonstrated and experimentally validated in application to additively manufactured lattice structures made from Polyamide 12 by laser sintering and from a highly viscous polymer by vat photopolymerization. For compression tests executed until densification or with unloading and at different rates, the beam simulations are in very good agreement with experiments. These results strongly indicate that the consideration of all nonlinear and inelastic effects is crucial to accurately model the finite deformation behavior of lattice structures. It can be concluded that this can be effectively attained using inelastic beam models, which opens the perspective for simulation-based design and optimization of lattices for practical applications.

In the present work, neural networks are applied to formulate parametrized hyperelastic constitutive models. The models fulfill all common mechanical conditions of hyperelasticity by construction. In particular, partially input convex neural network (pICNN) architectures are applied based on feed-forward neural networks. Receiving two different sets of input arguments, pICNNs are convex in one of them, while for the other, they represent arbitrary relationships which are not necessarily convex. In this way, the model can fulfill convexity conditions stemming from mechanical considerations without being too restrictive on the functional relationship in additional parameters, which may not necessarily be convex. Two different models are introduced, where one can represent arbitrary functional relationships in the additional parameters, while the other is monotonic in the additional parameters. As a first proof of concept, the model is calibrated to data generated with two differently parametrized analytical potentials, whereby three different pICNN architectures are investigated. In all cases, the proposed model shows excellent performance.

Self-reconfigurable robots present advanced solutions for various automation applications in domains, e.g., planetary exploration, rescue missions, cleaning, and maintenance. These robots have the ability to change their morphology according to given requirements or adapt to new circumstances, which, for example, can overcome constraints while navigating within a working environment. However, the autonomous navigation of self-reconfigurable robots is more complex than that of robots with fixed shape because of the intrinsic complexity of robot motions, especially in complicated obstacle environments. To address this challenge, we present a novel path planning method for reconfigurable robots in this study. The technique is inspired by the similarity between a robot motion path and a heat conduction path at the steady-state. In the heat transfer analysis domain, feasible moving locations are modeled as materials with high conductivity, while obstacles are considered thermal insulators, and the initial and destination positions are assigned as heat sink and heat source, respectively. The temperature profile and gradient calculated by finite element analysis are used to indicate the possible moving directions from the heat sink to the heat source. Based on the temperature gradient ascent, a step-wise conductivity reaching algorithm is developed to optimize robot paths using customized multi-objective functions that take the costs of morphology changes, path smoothness, and safety into account. The proposed path planning method is successfully applied to the hinged-tetro self-reconfigurable robot and demonstrated on several virtual environments and a real-world testbed environment.

Slender 1D structures are ubiquitous in nature and engineering and serve as building blocks for 3D structures at scales ranging from molecular to architectural. 3D printing enables fabrication of such structures with geometrical complexity that cannot be produced easily by traditional manufacturing methods, but comes with a cost of long building time and need for supporting structures during printing. Some of these limitations are overcome here through an approach that prints 1D rods with composite cross-sections, programmed to deform into a prescribed 3D shape simply upon heating. The straight or curved composite rods consist of a glassy polymer and an elastomer that are bonded to each other as a result of the manufacturing process; the latter is programmed with a compressive stress during the printing process. When heated, the stiff glassy polymer softens, resulting in release of the stress in the elastomer, and causes the 1D structure to deform into a new permanent 3D configuration. The cross-section of the composite rods can be designed to enable deformation modes of bending and twisting, a combination of which can guide the 1D rod into almost any 3D shape. With the use of a nonlinear thermomechanical computational model, several 3D rod structures are designed and demonstrated, highlighting the potential for increased functionality with material and time savings. Keywords: 3D printing, 4D printing, Rod structures, Self-assembly, Active composite