No Arabic abstract
Active structures have the ability to change their shape, properties, and functionality as a response to changing operational conditions, which makes them more versatile than their static counterparts. However, most active structures currently lack the capability to achieve multiple, different target states with a single input actuation or require a tedious material programming step. Furthermore, the systematic design and fabrication of active structures is still a challenge as many structures are designed by hand in a trial and error process and thus are limited by engineers knowledge and experience. In this work, a computational design and fabrication framework is proposed to generate structures with multiple target states for one input actuation that dont require a separate training step. A material dithering scheme based on multi-material 3D printing is combined with locally applied copper coil heating elements and sequential heating patterns to control the thermo-mechanical properties of the structures and switch between the different deformation modes. A novel topology optimization approach based on power diagrams is used to encode the different target states in the structure while ensuring the fabricability of the structures and the compatibility with the drop-in heating elements. The versatility of the proposed framework is demonstrated for four different example structures from engineering and computer graphics. The numerical and experimental results show that the optimization framework can produce structures that show the desired motion, but experimental accuracy is limited by current fabrication methods. The generality of the proposed method makes it suitable for the development of structures for applications in many different fields from aerospace to robotics to animated fabrication in computer graphics.
The data-driven computing paradigm initially introduced by Kirchdoerfer and Ortiz (2016) enables finite element computations in solid mechanics to be performed directly from material data sets, without an explicit material model. From a computational effort point of view, the most challenging task is the projection of admissible states at material points onto their closest states in the material data set. In this study, we compare and develop several possible data structures for solving the nearest-neighbor problem. We show that approximate nearest-neighbor (ANN) algorithms can accelerate material data searches by several orders of magnitude relative to exact searching algorithms. The approximations are suggested by--and adapted to--the structure of the data-driven iterative solver and result in no significant loss of solution accuracy. We assess the performance of the ANN algorithm with respect to material data set size with the aid of a 3D elasticity test case. We show that computations on a single processor with up to one billion material data points are feasible within a few seconds execution time with a speedup of more than 106 with respect to exact k-d trees.
Data-driven design of mechanical metamaterials is an increasingly popular method to combat costly physical simulations and immense, often intractable, geometrical design spaces. Using a precomputed dataset of unit cells, a multiscale structure can be quickly filled via combinatorial search algorithms, and machine learning models can be trained to accelerate the process. However, the dependence on data induces a unique challenge: An imbalanced dataset containing more of certain shapes or physical properties can be detrimental to the efficacy of data-driven approaches. In answer, we posit that a smaller yet diverse set of unit cells leads to scalable search and unbiased learning. To select such subsets, we propose METASET, a methodology that 1) uses similarity metrics and positive semi-definite kernels to jointly measure the closeness of unit cells in both shape and property spaces, and 2) incorporates Determinantal Point Processes for efficient subset selection. Moreover, METASET allows the trade-off between shape and property diversity so that subsets can be tuned for various applications. Through the design of 2D metamaterials with target displacement profiles, we demonstrate that smaller, diverse subsets can indeed improve the search process as well as structural performance. By eliminating inherent overlaps in a dataset of 3D unit cells created with symmetry rules, we also illustrate that our flexible method can distill unique subsets regardless of the metric employed. Our diverse subsets are provided publicly for use by any designer.
Specific strength (strength/density) is a crucial factor while designing high load bearing architecture in areas of aerospace and defence. Strength of the material can be enhanced by blending with high strength component or, by compositing with high strength fillers but both the options has limitations such as at certain load, materials fail due to poor filler and matrix interactions. Therefore, researchers are interested in enhancing strength of materials by playing with topology/geometry and therefore nature is best option to mimic for structures whereas, complexity limits nature mimicked structures. In this paper, we have explored Zeolite-inspired structures for load bearing capacity. Zeolite-inspired structure were obtained from molecular dynamics simulation and then fabricated via Fused deposition Modeling. The atomic scale complex topology from simulation is experimentally synthesized using 3D printing. Compressibility of as-fabricated structures was tested in different direction and compared with simulation results. Such complex architecture can be used for ultralight aerospace and automotive parts.
We describe a combined experimental and theoretical investigation of shape-morphing structures assembled by actuating composite (Janus) fibers, taking into account multiple relevant factors affecting shape transformations, such as strain rate, composition, and geometry of the structures. Starting with simple bending experiments, we demonstrate the ways to attain multiple out-of-plane shapes of closed rings and square frames. Through combining theory and simulation, we examine how the mechanical properties of Janus fibers affect shape transitions. This allows us to control shape changes and to attain target 3D shapes by precise tuning of the material properties and geometry of the fibers. Our results open new perspectives of design of advanced mechanical metamaterials capable to create elaborate structures through sophisticated actuation modes.
Flexible robotics are capable of achieving various functionalities by shape morphing, benefiting from their compliant bodies and reconfigurable structures. Here we construct and study a class of origami springs generalized from the known interleaved origami spring, as promising candidates for shape morphing in flexible robotics. These springs are found to exhibit nonlinear stretch-twist coupling and linear/nonlinear mechanical response in the compression/tension region, analyzed by the demonstrated continuum mechanics models, experiments, and finite element simulations. To improve the mechanical performance such as the damage resistance, we establish an origami rigidization method by adding additional creases to the spring system. Guided by the theoretical framework, we experimentally realize three types of flexible robotics -- origami spring ejectors, crawlers, and transformers. These robots show the desired functionality and outstanding mechanical performance. The proposed concept of origami-aided design is expected to pave the way to facilitate the diverse shape morphing of flexible robotics.