Multiscale Design and Fabrication of a Reliably Manufacturable Negative Stiffness Metamaterial

A negative stiffness (NS) metamaterial in which the damping of a material is significantly increased without decreasing its stiffness is designed and manufactured. This is accomplished by embedding a small volume fraction of precisely designed inclusions within a host material. These inclusions with micron-scale features are compliant mechanisms that exhibit negative stiffness. By macroscopically tuning the pre?strain of the metamaterial via mechanical loading, the embedded NS inclusions operate about a constrained buckling instability. Unlike other systems that dissipate energy primarily through large?amplitude deformation of nonlinear structures, this metamaterial dissipates energy by amplifying linear strains in the viscoelastic host material. When further macroscopic vibrational excitation is applied, the inclusions amplify the strains of the surrounding viscoelastic medium. This results in enhanced broadband dissipation of mechanical energy when compared to voided or neat comparison media. These NS metamaterials are designed with a multi-level, set-based design approach that leverages machine learning algorithms, specifically Bayesian network classifiers, to create inverse maps of the design space at each level, thereby supporting design exploration. The inclusions are fabricated with a micro-stereolithography process due to its high-resolution. To ensure that resulting designs are reliably manufacturable, manufacturing variability is quantified and incorporated into the design process. The metamaterial is assembled and tested, and results demonstrate broadband damping capabilities from a passive, mechanically tunable metamaterial.