  Auxetics refer to a class of engineered structures which exhibit an overall
negative Poisson's ratio. These structures open up various potential
opportunities in impact resistance, high energy absorption, and flexible
robotics, among others. Interestingly, auxetic structures could also be
tailored to provide passive adaptation to changes in environmental stimuli --
an adaptation of this concept is explored in this paper in the context of
designing a novel load-adaptive gripper system.
  Defining the design in terms of repeating parametric unit cells from which
the finite structure can be synthesized presents an attractive
computationally-efficient approach to designing auxetic structures. This
approach also decouples the optimization cost and the size of the overall
structure, and avoids the pitfalls of system-scale design e.g., via topology
optimization. In this paper, a surrogate-based design optimization framework is
presented to implement the concept of passively load-adaptive structures (of
given outer shape) synthesized from auxetic unit cells. Open-source meshing,
FEA and Bayesian Optimization tools are integrated to develop this
computational framework, enhancing it adopt-ability and extensibility.
Demonstration of the concept and the underlying framework is performed by
designing a simplified robotic gripper, with the objective to maximize the
ratio of towards-load (gripping) horizontal displacement to the load-affected
vertical displacement. Optimal auxetic cell-based design generated thereof is
found to be four times better in terms of exhibited contact reaction force when
compared to a design obtained with topology optimization that is subjected to
the same specified maximum loading.
