Monolithic binary stiffness building blocks for mechanical digital machines

Kuppens, P.R.; Bessa, Miguel A.; Herder, Just L.; Hopkins, Jonathan B.

doi:10.1016/j.eml.2020.101120

Cited by 18 publications

(18 citation statements)

References 55 publications

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“…There are many ways that the stiffness of a beam could be tuned to enable MNN learning. Principles of jamming ( 35 ) phase changing ( 36 ), static balancing ( 37 ), and electrorheology ( 38 ), among other approaches ( 39 ), could be used. Approaches that enable beams to continue exhibiting their prescribed stiffness without external influence (e.g., electrical power, magnetic fields, or temperature) are preferable for MNN applications because such networks physically store a kind of mechanical “muscle memory” in their architecture for manifesting the desired behaviors previously learned.…”

Section: Resultsmentioning

confidence: 99%

Mechanical neural networks: Architected materials that learn behaviors

2022

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Aside from some living tissues, few materials can autonomously learn to exhibit desired behaviors as a consequence of prolonged exposure to unanticipated ambient loading scenarios. Still fewer materials can continue to exhibit previously learned behaviors in the midst of changing conditions (e.g., rising levels of internal damage, varying fixturing scenarios, and fluctuating external loads) while also acquiring new behaviors best suited for the situation at hand. Here, we describe a class of architected materials, called mechanical neural networks (MNNs), that achieve such learning capabilities by tuning the stiffness of their constituent beams similar to how artificial neural networks (ANNs) tune their weights. An example lattice was fabricated to demonstrate its ability to learn multiple mechanical behaviors simultaneously, and a study was conducted to determine the effect of lattice size, packing configuration, algorithm type, behavior number, and linear-versus-nonlinear stiffness tunability on MNN learning as proposed. Thus, this work lays the foundation for artificial-intelligent (AI) materials that can learn behaviors and properties.

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Section: Resultsmentioning

confidence: 99%

Mechanical neural networks: Architected materials that learn behaviors

2022

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“…For example, a planar basic logical structure is constructed in Supplementary Note 3 . Conventional 2D microfabrication techniques such as LIGA and silicon micromachining can be used to fabricate such planar system 6 , 40 . Therefore, a cheaper method to construct a micro ReMM can be obtained by improving its geometry design, i.e., redesigning a planar ReMM.…”

Section: Resultsmentioning

confidence: 99%

A mechanical metamaterial with reprogrammable logical functions

et al. 2021

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Embedding mechanical logic into soft robotics, microelectromechanical systems (MEMS), and robotic materials can greatly improve their functional capacity. However, such logical functions are usually pre-programmed and can hardly be altered during in-life service, limiting their applications under varying working conditions. Here, we propose a reprogrammable mechanological metamaterial (ReMM). Logical computing is achieved by imposing sequential excitations. The system can be initialized and reprogrammed via selectively imposing and releasing the excitations. Realization of universal combinatorial logic and sequential logic (memory) is demonstrated experimentally and numerically. The fabrication scalability of the system is also discussed. We expect the ReMM can serve as a platform for constructing reusable and multifunctional mechanical systems with strong computation and information processing capability.

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“…Bistable compliant mechanisms (BCMs) are a special type of CM that can achieve two stable states via the energy stored in their flexures [128]. They have been utilized in different fields, such as switches, closure valves, or circuit breakers [129]. Some hand orthoses, such as SJS [52], operate in both the OFF and ON state, and these BCMs can be utilized as monolithic light switches to shift between states.…”

Section: Compliant Mechanismmentioning

confidence: 99%

4D printing of soft orthoses for tremor suppression

et al. 2022

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Tremor is an involuntary and oscillatory movement disorder that makes daily activities difficult for affected patients. Hand tremor-suppression orthoses are noninvasive, wearable devices designed to mitigate tremors. Various studies have shown that these devices are effective, economical, and safe; however, they have drawbacks such as large weight, awkward shape, and rigid parts. This study investigates different types of tremor-suppression orthoses and discusses their efficiency, mechanism, benefits, and disadvantages. First, various orthoses (with passive, semi-active, and active mechanisms) are described in detail. Next, we look at how additive manufacturing (AM) has progressed recently in making sensors and actuators for application in tremor orthoses. Then, the materials used in AM are further analyzed. It is found that traditional manufacturing problems can be solved with the help of AM techniques, like making orthoses that are affordable, lighter, and more customizable. Another concept being discussed is using smart materials and AM methods, such as four-dimensional (4D) printing, to make orthoses that are more comfortable and efficient. Graphic abstract

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Monolithic binary stiffness building blocks for mechanical digital machines

Cited by 18 publications

References 55 publications

Mechanical neural networks: Architected materials that learn behaviors

Mechanical neural networks: Architected materials that learn behaviors

A mechanical metamaterial with reprogrammable logical functions

4D printing of soft orthoses for tremor suppression

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