Project description:Mechanical metamaterials exhibit unusual properties through the shape and movement of their engineered subunits. This work presents a new investigation of the Poisson's ratios of a family of cellular metamaterials based on Kirigami design principles. Kirigami is the art of cutting and folding paper to obtain 3D shapes. This technique allows us to create cellular structures with engineered cuts and folds that produce large shape and volume changes, and with extremely directional, tuneable mechanical properties. We demonstrate how to produce these structures from flat sheets of composite materials. By a combination of analytical models and numerical simulations we show how these Kirigami cellular metamaterials can change their deformation characteristics. We also demonstrate the potential of using these classes of mechanical metamaterials for shape change applications like morphing structures.
Project description:Inspired by the art of paper cutting, kirigami provides intriguing tools to create materials with unconventional mechanical and morphological responses. This behavior is appealing in multiple applications such as stretchable electronics and soft robotics and presents a tractable platform to study structure-property relationships in material systems. However, mechanical response is typically controlled through a single or fractal cut type patterned across an entire kirigami sheet, limiting deformation modes and tunability. Here we show how hybrid patterns of major and minor cuts creates new opportunities to introduce boundary conditions and non-prismatic beams to enable highly tunable mechanical responses. This hybrid approach reduces stiffness by a factor of ~30 while increasing ultimate strain by a factor of 2 (up to 750% strain) relative to single incision patterns. We present analytical models and generate general design criteria that is in excellent agreement with experimental data from nanoscopic to macroscopic systems. These hybrid kirigami materials create new opportunities for multifunctional materials and structures, which we demonstrate with stretchable kirigami conductors with nearly constant electrical resistance up to >400% strain and magnetoactive actuators with extremely rapid response (>10,000% strain s-1) and high, repeatable elongation (>300% strain).
Project description:Many real-world applications, including adaptive radar scanning and smart stealth, require reconfigurable multifunctional devices to simultaneously manipulate multiple degrees of freedom of electromagnetic (EM) waves in an on-demand manner. Recently, kirigami technique, affording versatile and unconventional structural transformation, has been introduced to endow metamaterials with the capability of controlling EM waves in a reconfigurable manner. Here, we report for a kirigami-inspired sparse meta-architecture, with structural density of 1.5% in terms of the occupation space, for adaptive invisibility based on independent operations of frequency, bandwidth, and amplitude. Based on the general principle of dipolar management via structural reconstruction of kirigami-inspired meta-architectures, we demonstrate reconfigurable invisibility management with abundant EM functions and a wide tuning range using three enantiomers (A, B, and C) of different geometries characterized by the folding angle β. Our strategy circumvents issues of limited abilities, narrow tuning range, extreme condition, and high cost raised by available reconfigurable metamaterials, providing a new avenue toward multifunctional smart devices.
Project description:Mechanical metamaterials are known for their prominent mechanical characteristics such as programmable deformation that are due to periodic microstructures. Recent research trends have shifted to utilizing mechanical metamaterials as structural substrates to integrate with functional materials for advanced functionalities beyond mechanical, such as active sensing. This study reports on the ultra-stretchable kirigami piezo-metamaterials (KPM) for sensing coupled large deformations caused by in- and out-of-plane displacements using the lead zirconate titanate (PZT) and barium titanate (BaTiO3 ) composite films. The KPM are fabricated by uniformly compounding and polarizing piezoelectric particles (i.e., PZT and BaTiO3 ) in silicon rubber and structured by cutting the piezoelectric rubbery films into ligaments. Characterizes the electrical properties of the KPM and investigates the bistable mechanical response under the coupled large deformations with the stretching ratio up to 200% strains. Finally, the PZT KPM sensors are integrated into wireless sensing systems for the detection of vehicle tire bulge, and the non-toxic BaTiO3 KPM are applied for human posture monitoring. The reported kirigami piezo-metamaterials open an exciting venue for the control and manipulation of mechanically functional metamaterials for active sensing under complex deformation scenarios in many applications.
Project description:The rapid development of radio frequency (RF) components requires smart multifunctional materials that can adapt their physical shapes and properties according to the environment. While most current reconfigurable systems provide limited flexibility with high manufacturing cost, this research proposes to harness the transformable properties of kirigami-inspired multistable mechanical metasurfaces that can repeatedly deform and lock into different configurations to realize a novel class of low-cost reconfigurable electromagnetic structures with a broad design space. The metasurfaces are formed by designing kinematic-based unit cells with metallised coating that can provide adjustable resonant electromagnetic (EM) properties while rotating with respect to each other. Tailoring the cut length and geometry parameters of the patterns, we demonstrate programming of the topologies and shapes of different configurations. The influence of critical parameters on the structural multistability is illustrated by means of both a simplified energy model and finite element simulations. As examples of the reconfigurable electromagnetic devices that can be realized, we report the development of a tuneable half-wave dipole and two frequency selective surface (FSS) designs featuring isotropic and anisotropic responses. While the kirigami dipole can be tuned by mechanically stretching its arms, the FSSs exhibit distinct transmittance and reflectance spectra in each of the kirigami patterns stable states. The functionality of these kirigami devices is validated both by full-wave EM simulations and experiments. The proposed transformable structures can be mechanically actuated to tune the EM response in frequency or induce anisotropies for wave propagation.
Project description:Summary Stress-strain constitutive relations and Poisson’s ratios are fundamental properties of naturally occurring materials, based on which their mechanical applications can be designed. The lack of tailorability and restricted margin for such critical properties severely limit the bounds of achievable multi-functional engineering designs using conventional materials. Through analytical and numerical analyses, supported by elementary-level physical experiments, we have proposed a kirigami-inspired hybrid metamaterial with programmable deformation-dependent stiffness and mixed-mode multidirectional auxeticity. The metamaterial can transition from a phase of low stiffness to a contact induced phase that brings forth an extensive rise in stiffness. Uniform and graded configurations of multi-layer tessellated material are developed to modulate the constitutive law of the metastructure with augmented programmability in two- and three-dimensional spaces. The proposed metamaterial will lead to extreme lightweight functional designs for impact resistance, shape morphing, multidirectional deformation, vibration and wave propagation control, where the capabilities of intrinsic material can be most optimally exploited. Graphical abstract Highlights • Kirigami-inspired hybrid metamaterial with contact-induced programmable features• Deformation-dependent stiffness and mixed-mode multidirectional auxeticity• Uniform and graded configurations of multi-layer tessellated materials• Extreme lightweight functional designs for most optimal material utilization Mechanics of materials; Mechanical property; Metamaterials
Project description:The rise of machine learning has fueled the discovery of new materials and, especially, metamaterials-truss lattices being their most prominent class. While their tailorable properties have been explored extensively, the design of truss-based metamaterials has remained highly limited and often heuristic, due to the vast, discrete design space and the lack of a comprehensive parameterization. We here present a graph-based deep learning generative framework, which combines a variational autoencoder and a property predictor, to construct a reduced, continuous latent representation covering an enormous range of trusses. This unified latent space allows for the fast generation of new designs through simple operations (e.g., traversing the latent space or interpolating between structures). We further demonstrate an optimization framework for the inverse design of trusses with customized mechanical properties in both the linear and nonlinear regimes, including designs exhibiting exceptionally stiff, auxetic, pentamode-like, and tailored nonlinear behaviors. This generative model can predict manufacturable (and counter-intuitive) designs with extreme target properties beyond the training domain.
Project description:When individuals interact with each other and meaningfully contribute toward a common goal, it results in a collaboration. The artifacts resulting from collaborations are best captured using a hypergraph model, whereas the relation of who has collaborated with whom is best captured via an abstract simplicial complex (SC). We propose a generative algorithm GENESCs for SCs modeling fundamental collaboration relations. The proposed network growth process favors attachment that is preferential not to an individual's degree, i.e., how many people has he/she collaborated with, but to his/her facet degree, i.e., how many maximal groups or facets has he/she collaborated within. Based on our observation that several real-world facet size distributions have significant deviation from power law-mainly since larger facets tend to subsume smaller ones-we adopt a data-driven approach. We prove that the facet degree distribution yielded by GENESCs is power law distributed for large SCs and show that it is in agreement with real world co-authorship data. Finally, based on our intuition of collaboration formation in domains such as collaborative scientific experiments and movie production, we propose two variants of GENESCs based on clamped and hybrid preferential attachment schemes, and show that they perform well in these domains.
Project description:Networks are a powerful abstraction with applicability to a variety of scientific fields. Models explaining their morphology and growth processes permit a wide range of phenomena to be more systematically analysed and understood. At the same time, creating such models is often challenging and requires insights that may be counter-intuitive. Yet there currently exists no general method to arrive at better models. We have developed an approach to automatically detect realistic decentralised network growth models from empirical data, employing a machine learning technique inspired by natural selection and defining a unified formalism to describe such models as computer programs. As the proposed method is completely general and does not assume any pre-existing models, it can be applied "out of the box" to any given network. To validate our approach empirically, we systematically rediscover pre-defined growth laws underlying several canonical network generation models and credible laws for diverse real-world networks. We were able to find programs that are simple enough to lead to an actual understanding of the mechanisms proposed, namely for a simple brain and a social network.
Project description:This paper presents a biologically plausible generative model and inference scheme that is capable of simulating communication between synthetic subjects who talk to each other. Building on active inference formulations of dyadic interactions, we simulate linguistic exchange to explore generative models that support dialogues. These models employ high-order interactions among abstract (discrete) states in deep (hierarchical) models. The sequential nature of language processing mandates generative models with a particular factorial structure-necessary to accommodate the rich combinatorics of language. We illustrate linguistic communication by simulating a synthetic subject who can play the 'Twenty Questions' game. In this game, synthetic subjects take the role of the questioner or answerer, using the same generative model. This simulation setup is used to illustrate some key architectural points and demonstrate that many behavioural and neurophysiological correlates of linguistic communication emerge under variational (marginal) message passing, given the right kind of generative model. For example, we show that theta-gamma coupling is an emergent property of belief updating, when listening to another.