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

Yuning Zhang, Aditya Deshmukh, Kon-Well Wang
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引用次数: 0

Abstract

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.
基于物理储层计算的自适应声子元结构力学智能实验实现
实验研究了将机械智能集成到机械元结构中用于自适应波动控制。我们利用周期性屈曲梁模块创建了一个声子元结构原型,该模块通过使用线性位移执行器的长度重新配置,具有高度可调的波传播特性。通过利用物理油藏计算框架,我们证明了所提出的元结构可以识别和自适应不同的输入,通过决策适当的驱动来重新配置自己,以实现智能波阻塞任务。总体而言,本研究为利用物理计算和学习构建和集成结构中的功能机械智能提供了一种有希望的方法,并为下一代自适应结构和材料系统创造了新的方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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