Programmable multistable kirigami chain: Decoupling energy barrier and snapping force/displacement in a unified topology

Arraying bistable cells in a chain results in a multistable mechanical structure that is capable of sequential deformation paths and structural reconfigurability. However, to customize the multistability, for example, a prescribed energy barrier with variable snapping force/displacement, is constrained by the difficulty of decoupling energy from kinematic characteristics since they are always governed by the same geometric parameters. This paper proposes a bistable kirigami cut topology to realize decoupling. Cut parameters are classified into independent groups, corresponding to objective energy barrier or force/displacement, which are consequently independent also. On this basis, a machine learning (ML) and genetic algorithm (GA) based approach is presented to guide the inverse design of the nonlinear responses. Optimal individual kirigami cells that meet decoupled multi-objectives such as energy barrier, snapping force and stable displacement are obtained to construct kirigami chains. The conceivable deformation sequences, configurations and energy/force-displacement curves are validated by both the simulation and experimental results. The proposed mechanism decoupled strategy and inverse design framework open up new horizons for programming energy landscape and provide a general recipe for tailoring multistability in metamaterials.