Zhang Yuning, Deshmukh Aditya, Wang Kon-Well
Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA.
Adv Sci (Weinh). 2023 Dec;10(34):e2305074. doi: 10.1002/advs.202305074. Epub 2023 Oct 23.
Recent advances in autonomous systems have prompted a strong demand for the next generation of adaptive structures and materials to possess built-in intelligence in their mechanical domain, the so-called mechano-intelligence (MI). Previous MI attempts mainly focused on specific case studies and lacked a systematic foundation in effectively and efficiently constructing and integrating different intelligent functions. Here, a new approach is uncovered to create multifunctional MI in adaptive structures using physical reservoir computing (PRC). That is, to concurrently embody computing power and the key elements of intelligence, namely perception, decision-making, and commanding, directly in the mechanical domain, advancing from conventional reliance on add-on computers and massive electronics. As an exemplar platform, a mechanically intelligent phononic metastructure is developed by harnessing its high-degree-of-freedom nonlinear dynamics as PRC power. Through analyses and experiments, multiple intelligent structural functions are demonstrated ranging from self-tuning wave controls to wave-based logic gates. This research provides the much-needed basis for creating future smart structures and materials that greatly surpass the state of the art-such as lower power consumption, more direct interactions, and better survivability in harsh environments or under cyberattacks. Moreover, it enables the addition of new functions and autonomy to systems without overburdening the onboard computers.
自主系统的最新进展引发了对下一代自适应结构和材料的强烈需求,使其在机械领域具备内置智能,即所谓的机械智能(MI)。先前的机械智能尝试主要集中在特定案例研究上,在有效且高效地构建和集成不同智能功能方面缺乏系统基础。在此,发现了一种利用物理水库计算(PRC)在自适应结构中创建多功能机械智能的新方法。也就是说,直接在机械领域同时体现计算能力以及智能的关键要素,即感知、决策和指令,摆脱传统上对附加计算机和大量电子设备的依赖。作为一个示例平台,通过利用其高自由度非线性动力学作为PRC动力,开发了一种机械智能声子超结构。通过分析和实验,展示了多种智能结构功能,从自调谐波控制到基于波的逻辑门。这项研究为创造未来智能结构和材料提供了急需的基础,这些智能结构和材料将大大超越现有技术水平,例如更低的功耗、更直接的交互以及在恶劣环境或网络攻击下更好的生存能力。此外,它能够在不增加机载计算机负担的情况下为系统添加新功能和自主性。