Shape memory alloy constitutive models have been shown to accurately predict 1-D and 3-D material response under general thermomechanical loading. As with any constitutive model, however, the degree to which simulation results match experimental data is dependent on the accurate calibration of model parameters. This work presents a general framework for the identification of SMA material parameters using numerical optimization methods and experimental results that include both 1-D data (i.e., stress-strain and strain-temperature line plots) as well as 2-D digital image correlation (DIC) strain field data. The optimization framework is verified using 1-D and 3-D finite-element-based simulated results as pseudo-experimental data. The study shows that the proposed optimization methods can identify SMA parameters in an automated fashion using data taken from multiple types of experiment, identifying parameters that fit very closely to the pseudo-experimental data.

This paper presentsa technique for trajectory planning based on continuously parameterized high-level actions (motion primitives) of variable duration. This technique leverages deep reinforcement learning (Deep RL) to formulate a policy which is suitable for real-time implementation. There is no separation of motion primitive generation and trajectory planning: each individual short-horizon motion is formed during the Deep RL training to achieve the full-horizon objective. Effectiveness of the technique is demonstrated numerically on a well-studied trajectory generation problem and a planning problem on a known obstacle-rich map. This paper also develops a new loss function term for policy-gradient-based Deep RL, which is analogous to an anti-windup mechanism in feedback control. We demonstrate the inclusion of this new term in the underlying optimization increases the average policy return in our numerical example.

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Recent tendencies in architecture take a unique point of view, with aesthetically novel and unnatural sensibilities emerging from a close scrutiny and study of apparently natural systems.These tendencies are being driven by mathematical and computational abstractions that transform the way we understand the matterinformation relationship.This project was inspired by Op Art, a twentieth century art movement and style in which artists sought to create an impression of movement on an image surface by means of an optical illusion. Passive elements consisting of composite laminates were produced with the goal of creating lightweight, semi-rigid, and nearly transparent pieces. The incorporation of active materials comprised a unique aspect of this project: the investigation of surface movement through controlled and repeatable deformation of the composite structure using shape memory alloy (SMA) wiring technology.The integration of composite materials with SMA wiring and Arduino automation control resulted in an architectural wall that incorporated perceptual and actual motion.