Discriminative Transition Sequences of Origami Metamaterials for Mechanologic

Abstract

With the unique merit of exhibiting variable spectral gaps at different stable configurations, multistable mechanical metamaterials have facilitated extensive functions and applications, including phononic bandgap tuning [1,2] and broadband vibration control. [3,4] Among these practices, the multistable metamaterials, which are fundamentally nonlinear in their constitutive profiles, are mainly operating in linear regimes within small deformations around different stable equilibria between configuration transitions. On the other hand, other prospects, such as nonreciprocal wave transmission, [5][6][7] impact energy trapping, [8][9][10] shock isolation, [11,12] and transition signal propagation, [13][14][15] have leveraged the nonlinear feature of global multistability, particularly the snap-through transitions among different stable configurations. Recently, there is a growing interest in harnessing multistability for mechanical logic gates [16][17][18] and mechanical memory devices [19,20] by correlating the mechanical configurations with their digital counterparts. Upon external inputs, the logic operation is determined by the sequence of configuration transitions. While these outcomes are intriguing, the current stateof-the-art technology mainly exploited transitions in an ad hoc manner, and the underlying mechanics of a transition sequence and the corresponding triggering methods are often not well understood. In other words, systematic and comprehensive investigations into the global transition sequences have not been pursued, which is a major bottleneck that severely limits the robust realization of the many rich functions of multistability.As a design motif, origami, the ancient art of transforming flat sheets into a sophisticated sculpture through folding, provides potentials in building multistable mechanical metamaterials owing to its large design space and intrinsic geometric nonlinearity. [21] In addition, the scale-independence of the mechanical properties of origami allows it to work at multiple scales, including macroscopic and microscopic scales. The existing precision machining techniques [22] provide us with the possibility to fabricate miniature folding devices. Foreseeable applications include mechanical memory devices, [20,23] mechanologic, [18,24,25] and robotics. [26][27][28][29] Recently, by incorporating multiple stacked Miura-ori units via a novel stacking strategy, [30,31] a new "stacked Miura-ori-variant (SMOV)" structure is created. With unique multistability in inclined and curved directions and multiple configurations, the SMOV becomes a strong candidate for developing smart mechanical metamaterials with directional, configurational, and functional adaptability.

Embodying Multifunctional Mechano‐Intelligence in and Through Phononic Metastructures Harnessing Physical Reservoir Computing

“…Furthermore, we harness the wave manipulation functions to explore wave-based mechano-logic and successfully achieve all the basic logic gates by abstracting the wave transmission characteristics as mechanical bits. Building on these findings on the PM platform, our approach can be expanded to a large number of other high-degreeof-freedom and nonlinear structures, such as origami structures, tensegrity structures, and soft robotics, [5,[22][23][24][25][26] which would have broad impact in various other engineering fields. Overall, this research is transformative in offering a systematic foundation toward multi-faceted and integrated mechano-intelligence and in breaking new paths for adaptive structures and material systems.…”

“…Typically, the realization of the mechano-logic involves the abstraction of mechanical bits, often achieved through multi-stability, dynamical phase transitions, or wave transmissions. [5][6][7][8][9][10][11][12] Among various mechano-logic approaches, the wave-based logic gates [10][11][12] that are built on wave propagation-based principles have shown advantages due to their potential for high-speed operation and parallel processing. However, a challenge in current wave-based mechano-logic studies is the ability to realize all basic logic gates, which serve as the building blocks for more intricate operations.…”

“…Previous studies have attempted to address certain aspects of intelligence into the design of various systems. [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19] While promising, these mechano-intelligence (MI) efforts have been primarily limited to specific cases with narrowly defined functions. Overall, a systematic foundation remains lacking for effective synthesizing and integrating the various intelligent elements, namely information perception, decisionmaking, and commanding, in multifunctional structures.…”

“…Reservoir computing employs a fixed-interconnection network as the reservoir and only requires training for the readout layer, resulting in low training cost. Moreover, due to their fixed nature, their physical implementation becomes very promising, which opens up the excellent possibility of using mechanical systems to conduct computing, [5,[22][23][24][25][26][27] ideal for our proposed MI. However, despite the great potential of the PRC framework, the efforts have mostly focused on computation only, and have not yet been utilized to construct MI to enable engineering-relevant functionalities.…”

Experimental realization of physical reservoir computing-based mechano-intelligence in self-adaptive phononic metastructures

“…On the other hand, the recent success in artificial neural networks has inspired an efficient mechanical computing paradigm known as physical reservoir computing [5][6][7][8][9] (PRC) by harnessing the high-dimensional and nonlinear dynamics of some mechanical structures as computational resources. By training an additional linear weighted readout layer, mechanical systems can learn complex input-output mappings, resulting in versatile and multi-tasking computing capabilities, which can serve as a core component of mechano-intelligence and lay the foundation for other intelligence aspects including sensing, decision-making and execution.…”