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Mechanical cloaks are materials engineered to manipulate the elastic responsearound objects to make them indistinguishable from their homogeneoussurroundings. Typically, methods based on material-parameter transformationsare used to design optical, thermal and electric cloaks. However, they are notapplicable in designing mechanical cloaks, since continuum-mechanics equationsare not form-invariant under general coordinate transformations. As a result,existing design methods for mechanical cloaks have so far been limited to anarrow selection of voids with simple shapes. To address this challenge, wepresent a systematic, data-driven design approach to create mechanical cloakscomposed of aperiodic metamaterials using a large pre-computed unit celldatabase. Our method is flexible to allow the design of cloaks with variousboundary conditions, multiple loadings, different shapes and numbers of voids,and different homogeneous surroundings. It enables a concurrent optimization ofboth topology and properties distribution of the cloak. Compared toconventional fixed-shape solutions, this results in an overall better cloakingperformance, and offers unparalleled versatility. Experimental measurements on3D-printed structures further confirm the validity of the proposed approach.Our research illustrates the benefits of data-driven approaches in quicklyresponding to new design scenarios and resolving the computational challengeassociated with multiscale designs of functional structures. It could begeneralized to accommodate other applications that require heterogeneousproperty distribution, such as soft robots and implants design.

Mechanical Cloak via Data-Driven Aperiodic Metamaterial Design