Using binary-stiffness beams within mechanical neural-network metamaterials to learn

Abstract This work introduces the concept of applying binary-stiffness beams within a lattice to achieve a mechanical neural-network (MNN) metamaterial that learns its behaviors and properties with prolonged exposure to unanticipated ambient loading scenarios. Applying such beams to MNN metamaterials greatly increases their learning speed and simplifies the actuation demands, control circuitry, and optimization algorithms required by previously proposed concepts. A binary-stiffness beam design is proposed that uses principles of constraint manipulation and stiffness cancelation to achieve two switchable and discrete states of stiffness (i.e. binary stiffness) along its axis. The beam achieves a near-zero low-stiffness state and a large difference in stiffness between its high and low-stiffness states, which are both shown to be desirable attributes for learning mechanical behaviors. Simulations are conducted to characterize the effect of lattice size, the difference in stiffness between the constituent beam’s high and low-stiffness states, the magnitude of its low-stiffness state, and the number of simultaneously learned behaviors on MNN learning using binary-stiffness beams. Thus, this work provides a necessary step toward enabling practical artificial intelligent metamaterials.