Modeling and optimal design of Neuro‐Mechanical Shape Memory Devices

In this paper we describe the modeling and optimization of what we refer to as Neuro‐Mechanical Shape Memory Devices (NMSMDs). These are active mechanical structures which are designed to take on specific shapes in response to certain external stimuli. An NMSMD is a particular example of a Neuro‐Mechanical Network (NMN), a mechanical structure that consists of a network of simple but multifunctional elements. In the present work, each element contains an actuator and an artificial neuron, and when assembled into a structure the elements form an actuated truss with a superimposed recurrent neural network. The task of designing an NMSMD is cast as an optimization problem in which a measure of the error between the actual and desired shape for a number of given stimuli is minimized. The optimization problems are solved using a gradient based solver, and some numerical examples are provided to illustrate the results from the design process and some aspects of the proposed model.