  Mechanical cloaks are materials engineered to manipulate the elastic response
around objects to make them indistinguishable from their homogeneous
surroundings. Typically, methods based on material-parameter transformations
are used to design optical, thermal and electric cloaks. However, they are not
applicable in designing mechanical cloaks, since continuum-mechanics equations
are not form-invariant under general coordinate transformations. As a result,
existing design methods for mechanical cloaks have so far been limited to a
narrow selection of voids with simple shapes. To address this challenge, we
present a systematic, data-driven design approach to create mechanical cloaks
composed of aperiodic metamaterials using a large pre-computed unit cell
database. Our method is flexible to allow the design of cloaks with various
boundary conditions, multiple loadings, different shapes and numbers of voids,
and different homogeneous surroundings. It enables a concurrent optimization of
both topology and properties distribution of the cloak. Compared to
conventional fixed-shape solutions, this results in an overall better cloaking
performance, and offers unparalleled versatility. Experimental measurements on
3D-printed structures further confirm the validity of the proposed approach.
Our research illustrates the benefits of data-driven approaches in quickly
responding to new design scenarios and resolving the computational challenge
associated with multiscale designs of functional structures. It could be
generalized to accommodate other applications that require heterogeneous
property distribution, such as soft robots and implants design.
