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Recent advances in active materials and fabrication techniques have enabled the production of cyclically self-deployable metamaterials with an expanded functionality space. However, designing metamaterials that possess continuously tunable mechanical properties after self-deployment remains a challenge, notwithstanding its importance. Inspired by push puppets, we introduce an efficient design strategy to create reversibly self-deployable metamaterials with continuously tunable post-deployment stiffness and damping. Our metamaterial comprises contracting actuators threaded through beads with matching conical concavo-convex interfaces in networked chains. The slack network conforms to arbitrary shapes, but when actuated, it self-assembles into a preprogrammed configuration with beads gathered together. Further contraction of the actuators can dynamically tune the assembly's mechanical properties through the beads' particle jamming, while maintaining the overall structure with minimal change. We show that, after deployment, such metamaterials exhibit pronounced tunability in bending-dominated configurations: they can become more than 35 times stiffer and change their damping capability by over 50%. Through systematic analysis, we find that the beads' conical angle can introduce geometric nonlinearity, which has a major effect on the self-deployability and tunability of the metamaterial. Our work provides routes towards reversibly self-deployable, lightweight, and tunable metamaterials, with potential applications in soft robotics, reconfigurable architectures, and space engineering.
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The longevity of current joint replacements is limited by aseptic loosening, which is the primary cause of non-infectious failure for hip, knee, and ankle arthroplasty. Aseptic loosening is typically caused either by osteolysis from particulate wear, or by high shear stresses at the bone-implant interface from over-constraint. Our objective was to demonstrate feasibility of a compliant intramedullary stem that eliminates over-constraint without generating particulate wear. The compliant stem is built around a compliant mechanism that permits rotation about a single axis. We first established several models to understand the relationship between mechanism geometry and implant performance under a given angular displacement and compressive load. We then used a neural network to identify a design space of geometries that would support an expected 100-year fatigue life inside the body. We additively manufactured one representative mechanism for each of three anatomic locations, and evaluated these prototypes on a KR-210 robot. The neural network predicts maximum stress and torsional stiffness with 2.69% and 4.08% error respectively, relative to finite element analysis data. We identified feasible design spaces for all three of the anatomic locations. Simulated peak stresses for the three stem prototypes were below the fatigue limit. Benchtop performance of all three prototypes was within design specifications. Our results demonstrate the feasibility of designing patient- and joint-specific compliant stems that address the root causes of aseptic loosening. Guided by these results, we expect the use of compliant intramedullary stems in joint reconstruction technology to increase implant lifetime.
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Artroplastia de Substituição , Humanos , Interface Osso-ImplanteRESUMO
Aside from some living tissues, few materials can autonomously learn to exhibit desired behaviors as a consequence of prolonged exposure to unanticipated ambient loading scenarios. Still fewer materials can continue to exhibit previously learned behaviors in the midst of changing conditions (e.g., rising levels of internal damage, varying fixturing scenarios, and fluctuating external loads) while also acquiring new behaviors best suited for the situation at hand. Here, we describe a class of architected materials, called mechanical neural networks (MNNs), that achieve such learning capabilities by tuning the stiffness of their constituent beams similar to how artificial neural networks (ANNs) tune their weights. An example lattice was fabricated to demonstrate its ability to learn multiple mechanical behaviors simultaneously, and a study was conducted to determine the effect of lattice size, packing configuration, algorithm type, behavior number, and linear-versus-nonlinear stiffness tunability on MNN learning as proposed. Thus, this work lays the foundation for artificial-intelligent (AI) materials that can learn behaviors and properties.
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Aprendizagem , Redes Neurais de Computação , AlgoritmosRESUMO
Mechanical metamaterials have been designed to achieve custom Poisson's ratios via the deformation of their microarchitecture. These designs, however, have yet to achieve the capability of exhibiting Poisson's ratios that alternate by design both temporally and spatially according to deformation. This capability would enable dynamic shape-morphing applications including smart materials that process mechanical information according to multiple time-ordered output signals without requiring active control or power. Herein, both periodic and graded metamaterials are introduced that leverage principles of differential stiffness and self-contact to passively achieve sequential deformations, which manifest as user-specified alternating Poisson's ratios. An analytical approach is provided with a complementary software tool that enables the design of such materials in two- and three-dimensions. This advance in design capability is due to the fact that the tool computes sequential deformations more than an order of magnitude faster than contemporary finite-element packages. Experiments on macro- and micro-scale designs validate their predicted alternating Poisson's ratios.
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In this work, we demonstrate the high-throughput fabrication of 3D microparticles using a scanning two-photon continuous flow lithography (STP-CFL) technique in which microparticles are shaped by scanning the laser beam at the interface of laminar co-flows. The results demonstrate the ability of STP-CFL to manufacture high-resolution complex geometries of cell carriers that possess distinct regions with different functionalities. A new approach is presented for printing out-of-plane features on the microparticles. The approach eliminates the use of axial scanning stages, which are not favorable since they induce fluctuations in the flowing polymer media and their scanning speed is slower than the speed of galvanometer mirror scanners.
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In this work, a scalable automated approach for fabricating 3D microgranular crystals consisting of desired arrangements of microspheres using holographic optical tweezers and two-photon polymerization is introduced. The ability to position microspheres as desired within lattices of any configuration allows designers to engineer the behavior of new metamaterials that enable advanced applications (e.g., armor that mitigates or redirects shock waves, acoustic lens for underwater imaging, damage detection, and noninvasive surgery, acoustic cloaking, and photonic crystals). Currently, no self-assembly or automated approaches exist with the flexibility necessary to place specific microspheres at specific locations within a crystal. Moreover, most pick-and-place approaches require the manual assembly of spheres one by one and thus do not achieve the speed and precision required to repeatably fabricate practical volumes of engineered crystals. In this paper, the rapid assembly of 4.86 µm diameter silica spheres within differently packed 3D crystal-lattice examples of unprecedented size using fully automated optical tweezers is demonstrated. The optical tweezers independently and simultaneously assemble batches of spheres that are dispensed to the build site via an automated syringe pump where the spheres are then joined together within previously unattainable patterns by curing regions of photocurable prepolymer between each sphere using two-photon polymerization.
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Early examples of computers were almost exclusively based on mechanical devices. Although electronic computers became dominant in the past 60 years, recent advancements in three-dimensional micro-additive manufacturing technology provide new fabrication techniques for complex microstructures which have rekindled research interest in mechanical computations. Here we propose a new digital mechanical computation approach based on additively-manufacturable micro-mechanical logic gates. The proposed mechanical logic gates (i.e., NOT, AND, OR, NAND, and NOR gates) utilize multi-stable micro-flexures that buckle to perform Boolean computations based purely on mechanical forces and displacements with no electronic components. A key benefit of the proposed approach is that such systems can be additively fabricated as embedded parts of microarchitected metamaterials that are capable of interacting mechanically with their surrounding environment while processing and storing digital data internally without requiring electric power.
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Designing mechanical metamaterials is overwhelming for most computational approaches because of the staggering number and complexity of flexible elements that constitute their architecture-particularly if these elements don't repeat in periodic patterns or collectively occupy irregular bulk shapes. We introduce an approach, inspired by the freedom and constraint topologies (FACT) methodology, that leverages simplified assumptions to enable the design of such materials with ~6 orders of magnitude greater computational efficiency than other approaches (e.g., topology optimization). Metamaterials designed using this approach are called directionally compliant metamaterials (DCMs) because they manifest prescribed compliant directions while possessing high stiffness in all other directions. Since their compliant directions are governed by both macroscale shape and microscale architecture, DCMs can be engineered with the necessary design freedom to facilitate arbitrary form and unprecedented anisotropy. Thus, DCMs show promise as irregularly shaped flexure bearings, compliant prosthetics, morphing structures, and soft robots.
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Architected materials can achieve impressive shape-changing capabilities according to how their microarchitecture is engineered. Here we introduce an approach for dramatically advancing such capabilities by utilizing wrapped flexure straps to guide the rolling motions of tightly packed micro-cams that constitute the material's microarchitecture. This approach enables high shape-morphing versatility and extreme ranges of deformation without accruing appreciable increases in strain energy or internal stress. Two-dimensional and three-dimensional macroscale prototypes are demonstrated, and the analytical theory necessary to design the proposed materials is provided and packaged as a software tool. An approach that combines two-photon stereolithography and scanning holographic optical tweezers is demonstrated to enable the fabrication of the proposed materials at their intended microscale.
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The purpose of this work is to introduce three improvements to automated holographic-optical-tweezers systems that increase the number and speed of particles that can be manipulated simultaneously. First, we address path planning by solving a bottleneck assignment problem, which can reduce total move time by up to 30% when compared with traditional assignment problem solutions. Next, we demonstrate a new strategy to identify and remove undesired (e.g., misshapen or agglomerated) particles. Finally, we employ a controller that combines both closed- and open-loop automation steps, which can increase the overall loop rate and average particle speeds while also utilizing necessary process monitoring checks to ensure that particles reach their destinations. Using these improvements, we show fast reconfiguration of 100 microspheres simultaneously with a closed-loop control rate of 6, and 10 Hz by employing both closed- and open-loop steps. We also demonstrate the closed-loop assembly of a large pattern in a continuously flowing microchannel-based particle-delivery system. The proposed approach provides a promising path toward automatic and scalable assembly of microgranular structures.
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We report a new framework for a quantitative understanding of optical trapping (OT) particle handling dynamics. We present a novel three-dimensional particle-based model that includes optical, hydrodynamic, and inter-particle forces. This semi-empirical colloid model is based on an open-source simulation code known as LAMMPS (large-scale atomic/molecular massively parallel simulator) and properly recapitulates the full OT force profile beyond the typical linear approximations valid near the trap center. Simulations are carried out with typical system parameters relevant for our experimental holographic optical trapping (HOT) system, including varied particle sizes, trap movement speeds, and beam powers. Furthermore, we present a new experimental method for measuring both the stable and metastable boundaries of the optical force profile to inform or validate the model's underlying force profile. We show that our framework is a powerful tool for accurately predicting particle behavior in a practical experimental OT setup and can be used to characterize and predict particle handling dynamics within any arbitrary OT force profile.
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An error in a reference is reported and corrected.
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Demand continues to rise for custom-fabricated and engineered colloidal microparticles across a breadth of application areas. This paper demonstrates an improvement in the fabrication rate of high-resolution 3D colloidal particles by using two-photon scanning lithography within a microfluidic channel. To accomplish this, we present (1) an experimental setup that supports fast, 3D scanning by synchronizing a galvanometer, piezoelectric stage, and an acousto-optic switch, and (2) a new technique for modifying the laser's scan path to compensate for the relative motion of the rapidly-flowing photopolymer medium. The result is an instrument that allows for rapid conveyor-belt-like fabrication of colloidal objects with arbitrary 3D shapes and micron-resolution features.
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Ice floating on water is a great manifestation of negative thermal expansion (NTE) in nature. The limited examples of natural materials possessing NTE have stimulated research on engineered structures. Previous studies on NTE structures were mostly focused on theoretical design with limited experimental demonstration in two-dimensional planar geometries. In this work, aided with multimaterial projection microstereolithography, we experimentally fabricate lightweight multimaterial lattices that exhibit significant negative thermal expansion in three directions and over a temperature range of 170 degrees. Such NTE is induced by the structural interaction of material components with distinct thermal expansion coefficients. The NTE can be tuned over a large range by varying the thermal expansion coefficient difference between constituent beams and geometrical arrangements. Our experimental results match qualitatively with a simple scaling law and quantitatively with computational models.