Monolithic binary stiffness building blocks for mechanical digital machines

Journal Article (2021)
Author(s)

P.R. Kuppens (TU Delft - Mechatronic Systems Design)

M.A. Bessa (TU Delft - Team Georgy Filonenko)

J. L. Herder (TU Delft - Precision and Microsystems Engineering, TU Delft - Mechatronic Systems Design)

J.B. Hopkins (University of California)

Research Group
Mechatronic Systems Design
Copyright
© 2021 P.R. Kuppens, M.A. Bessa, J.L. Herder, J. B. Hopkins
DOI related publication
https://doi.org/10.1016/j.eml.2020.101120
More Info
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Publication Year
2021
Language
English
Copyright
© 2021 P.R. Kuppens, M.A. Bessa, J.L. Herder, J. B. Hopkins
Research Group
Mechatronic Systems Design
Volume number
42
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Abstract

We introduce two essential building blocks with binary stiffness for mechanical digital machines. The large scale fully compliant mechanisms have rectilinear and rotational kinematics and use a new V-shaped negative stiffness structure to create two extreme states of stiffness by static balancing. The use of a mechanical bistable switch allows us to toggle between near-zero-stiffness and high-stiffness states, effectively turning off and on stiffness. A stiffness reduction of 98.8% and 99.9% is achieved for linear and rotary motion over a range of 13.3% (20mm) and 0.4rad (23∘) respectively. Stiffness states can be reversibly changed by toggling the mechanical switch, or irreversibly by actuating the main stage. These binary stiffness mechanisms could set the stage for a new type of mechanical logic, adaptive and programmable metamaterials and other types of digital mechanical devices. Practical mechanical digital machines and materials require miniaturized and easily micro-manufactured components. We have therefore carefully considered scalability by integrating all required structures into a planar and monolithic architecture. This allows miniaturization and fabrication with conventional surface-micro-machining and additive manufacturing such as photolithography, two-photon lithography and fused deposition modeling.