This research experimentally investigates the integration of mechano-intelligence into mechanical metastructures for self-adaptive wave control. We created a phononic metastructure prototype utilizing periodic buckled beam modules that has highly adjustable wave propagation characteristics via length reconfiguration using a linear displacement actuator. By utilizing the physical reservoir computing framework, we show that the proposed metastructure can recognize and self-adapt to different inputs by making decisions on appropriate actuations to reconfigure itself to achieve an intelligent wave blocking task. Overall, this research provided a promising approach for constructing and integrating functional mechano-intelligence in structures harnessing physical computing and learning, and created a new direction for the next generation of adaptive structures and material systems.
With advances in new technologies, researchers are attempting to develop the next generation of adaptive structures with intelligence directly embedded within their mechanical domain, the so-called mechano-intelligence. These attempts will enable mechanical systems to perform intelligent tasks, such as sensing the environment, changing geometries, making decisions, and executing computation in an even more proactive and autonomous manner, as compared to traditional mechatronic systems. However, there is no systematic foundation in constructing and integrating different aspects of mechano-intelligence. To advance the state of art, this research proposes to enhance the mechano-intelligence in adaptive structures through a machine learning framework called Physical Reservoir Computing (PRC). We show that a tunable modular metastructure can learn from its own wave dynamics and adaptively tune its own band structure via PRC. In other words, the metastructure can sense different input waves, make decisions and output appropriate control commands to alter its own wave characteristics without digital signal processors and controllers, i.e., achieve autonomous and integrated mechano-intelligence. Overall, this research provides a novel method to achieve intelligent and adaptive vibration/wave control based on the concepts of physical computing and learning and forms the basis for multi-faceted functional-relevant mechano-intelligence to be embedded in future adaptive structures and material systems.
Recently, acoustic/elastic metasurfaces have gained increasing research interests due to their ability to control waves with compact and lightweight structures. A metasurface is a thin layer in the host medium composed of an array of subwavelength-scaled patterns, which introduces an abrupt phase shift in the wave propagation path and tailors wavefront based on generalized Snell’s law. The existing metasurfaces mainly depend on the linear dynamic behavior of the structures, while their nonlinear features have not been studied extensively. A couple recent attempts have shown means of introducing nonlinearity in acoustic metasurface designs, resulting in nonlinear effects such as second-harmonic generation (SHG). However, these studies mainly focus on generating and maximizing the higher-order harmonics, while the phase modulation and wavefront tailoring capability are less explored. Our study advances the state of the art and proposes a novel acoustic metasurface design with locally resonant nonlinear elements in the form of curved beams. We explore the nonlinear phenomenon, specifically SHG, of the proposed system using both analytical and numerical frameworks. Our results show that the proposed nonlinear metasurface can achieve SHG in the transmitted acoustic wavefield, and simultaneously demultiplex for different frequency components (i.e., split the second-harmonic component from the fundamental frequency component) by steering them into different directions. This study presents new theoretical and numerical platforms to explore the amplitude-dependent behavior of acoustic metasurfaces, expands their wavefront tailoring capabilities and functionalities, and develops new potentials towards efficient technologies to manipulate acoustic waves.
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