Hiromi Yasuda

We investigate the reconfigurability and tunability of the tessellation of Tachi-Miura Polyhedron (TMP), an origami-based cellular structure composed of bellows-like unit cells. Lattice-based three-dimensional mechanical metamaterials have recently received significant scientific interest due to their superior and unique mechanical performance compared to conventional materials. However, it is often challenging to achieve tunability and reconfigurability from these metamaterials, since their geometry and functionality tend to be pre-determined in the design and fabrication stage. Here, we utilize TMP's highly versatile phase-transforming and tessellating capabilities to design reconfigurable metamaterial architecture with tunable mechanical properties. The theoretical analyses and experiments with heat processing discover the wide range of the in-situ tunability of the metamaterial – specifically orders of magnitude change in effective density, Young's modulus, and Poisson's ratio – after its fabrication within the elastic deformation regime. We also witness a rather unique behavior of the inverse correlation between effective density and stiffness. This mechanical platform paves the way to design the metamaterial that can actively adapt to various external environments.

As an art of paper folding, origami has been widely explored by artists for centuries. Only in recent decades has it gained attention from mathematicians and engineers for its complex geometry and rich mechanical properties. The surge of origami-inspired metamaterials has opened a new window for designing materials and structures. Typically, to build origami structures, a sheet of material is folded according to the creaselines that are marked with compliant mechanisms. However, despite their importance in origami fabrication, such compliant mechanisms have been relatively unexplored in the setting of origami metamaterials. In this study, we explore the relationship between the design parameters of compliant mechanisms and origami mechanical properties. In particular, we employ single hinge crease and Kresling origami, representative examples of rigid and non-rigid origami units, fabricated using a double-stitch perforation compliant mechanism design. We conduct axial compression tests using different crease parameters and fit the result into the bar-hinge origami model consisting of axial and torsional springs. We extract the relationship between the spring coefficients and crease parameters using Gaussian process regression. Our result shows that the change in the crease parameter contributes significantly to each spring element in a very different manner, which suggests the fine tunability of the compliant mechanisms depending on the mode of deformation. In particular, the spring stiffness varies with the crease parameter differently for rigid and non-rigid origami, even when the same crease parameter is tuned. Furthermore, we report that the qualitative static response of the Kresling origami can be tuned between monostable and bistable, or linear and nonlinear, by only changing the crease parameter while keeping the same fold pattern geometry. We believe that our compiled result proffers a library and guidelines for choosing compliant mechanisms for the creases of origami mechanical metamaterials.[Figure not available: see fulltext.]

It is crucial to monitor the wingtip deflection of aircraft in real-time to ensure its aerodynamic functionality and structural safety. Also, the prediction of the wing tip deflection can be highly useful to provide aircraft with improved maneuverability and agile response to sudden events, such as gust and flutter. In this study, we propose a wingtip deflection monitoring/prediction method based on machine learning techniques. Specifically, we employ an aerodynamic solver to simulate an aeroelastic flutter behavior of a fixed-wing, and demonstrate the feasibility of our approach to predict oscillatory wingtip motion due to the flutter. To track and predict the wingtip deflection, we build network architectures composed of convolutional neural networks (CNNs) to analyze optically measured data and recurrent neural networks (RNNs) to process time-series information. As a result, we find the proposed technique is capable of monitoring and predicting wingtip deflection in an accurate and efficient manner. We expect this vision and machine-learning-based technique can complement existing sensor technologies to enhance the safety and maneuverability of aircraft.