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In this study, we present a method for generating a synthetic gauge field in origami metamaterials with continuously varying geometrical parameters. By modulating the mass term in the Dirac equation linearly, we create a synthetic gauge field in the vertical direction, which allows for the quantization of Landau levels through the generated pseudomagnetic field. Furthermore, we demonstrate the existence and robustness of the chiral zeroth Landau level. The unique elastic snake state is realized using the coupling between the zeroth and the first Landau levels. Our results, supported by theory and simulations, establish a feasible framework for generating pseudomagnetic fields in origami metamaterials with potential applications in waveguides and cloaking., Comment: 6 pages, 4 figures

In this study, we experimentally and numerically investigate the evolution of the tensile material properties of Discontinuous Fiber Composites (DFCs) with an increasing average number of platelets through the thickness for two different platelet widths. The results show that both the number of platelets and the platelet width have significant effects on the tensile modulus and strength. We find that not only the average mechanical properties but also their coefficients of variation change according to the different DFC mesostructures. To understand the relationship between material morphology at the mesoscale and corresponding material properties, we developed a random platelet mesostructure generation algorithm combined with explicit finite element models. Leveraging the computational tools, we find that moduli and strength increase with increasing average number of platelets through the thickness. The increasing trend continues until reaching an asymptotic limit at about 45 layers through the thickness for the narrow platelets and 27 layers for the square platelets. In the study, we address the importance of having accurate simulations of the mesostructure to match not only the average modulus and strength but also their associated coefficients of variation. We show that it is possible to accurately predict the tensile material properties of DFCs, including their B-basis design values. This is a quintessential condition for the adoption of DFCs in structural applications.

Origami-based mechanical metamaterials have recently received significant scientific interest due to their versatile and reconfigurable architectures. However, it is often challenging to account for all possible geometrical configurations of the origami assembly when each origami cell can take multiple phases. Here, we investigate the reconfigurability of a tessellation of origami-based cellular structures composed of bellows-like unit cells, specifically Tachi-Miura Polyhedron (TMP). One of the unique features of the TMP is that a single cell can take four different phases in a rigid foldable manner. Therefore, the TMP tessellation can achieve various shapes out of one originally given assembly. To assess the geometrical validity of the astronomical number of origami phase combinations, we build a graph-theoretical framework to describe the connectivity of unit cells and to analyze the reconfigurability of the tessellations. Our approach can pave the way to develop a systematic computational tool to design origami-based mechanical metamaterials with tailored properties., Comment: 20 pages and 8 figures

Advances in machine learning have revolutionized capabilities in applications ranging from natural language processing to marketing to health care. Here, we demonstrate the efficacy of machine learning in predicting chaotic behavior in complex nonlinear mechanical systems. Specifically, we use quasi-recurrent neural networks to predict extremely chaotic time series data obtained from multistable origami systems. Additionally, while machine learning is often viewed as a "black box", in this study we conduct hidden layer analysis to understand how the neural network can process not only periodic, but also chaotic data in an accurate manner. Also, our approach shows its effectiveness in characterizing and predicting chaotic dynamics in a noisy environment of vibrations without relying on a mathematical model of origami systems. Therefore, our method is fully data-driven and has the potential to be used for complex scenarios, such as the nonlinear dynamics of thin-walled structures and biological membrane systems.

Origami has recently received significant interest from the scientific community as a building block for constructing metamaterials. However, the primary focus has been placed on their kinematic applications, such as deployable space structures and sandwich core materials, by leveraging the compactness and auxeticity of planar origami platforms. Here, we present volumetric origami cells -- specifically triangulated cylindrical origami (TCO) -- with tunable stability and stiffness, and demonstrate their feasibility as non-volatile mechanical memory storage devices. We show that a pair of origami cells can develop a double-well potential to store bit information without the need of residual forces. What makes this origami-based approach more appealing is the realization of two-bit mechanical memory, in which two pairs of TCO cells are interconnected and one pair acts as a control for the other pair. Using TCO-based truss structures, we present an experimental demonstration of purely mechanical one- and two-bit memory storage mechanisms.