@article{haigh_crews_wang_buckner_2019, title={Multi-Objective Design Optimization of a Shape Memory Alloy Flexural Actuator}, volume={8}, ISSN={["2076-0825"]}, DOI={10.3390/act8010013}, abstractNote={This paper presents a computational model and design optimization strategy for shape memory alloy (SMA) flexural actuators. These actuators consist of curved SMA wires embedded within elastic structures; one potential application is positioning microcatheters inside blood vessels during clinical treatments. Each SMA wire is shape-set to an initial curvature and inserted along the neutral axis of a straight elastic member (cast polydimethylsiloxane, PDMS). The elastic structure preloads the SMA, reducing the equilibrium curvature of the composite actuator. Temperature-induced phase transformations in the SMA are achieved via Joule heating, enabling strain recovery and increased bending (increased curvature) in the actuator. Actuator behavior is modeled using the homogenized energy framework, and the effects of two critical design parameters (initial SMA curvature and flexural rigidity of the elastic sleeve) on activation curvature are investigated. Finally, a multi-objective genetic algorithm is utilized to optimize actuator performance and generate a Pareto frontier, which is subsequently experimentally validated.}, number={1}, journal={ACTUATORS}, author={Haigh, Casey D. and Crews, John H. and Wang, Shiquan and Buckner, Gregory D.}, year={2019}, month={Feb} }
 @inproceedings{crews_smith_hannen_2013, title={Development of robust control algorithms for shape memory alloy bending actuators}, DOI={10.1115/smasis2012-7989}, abstractNote={In this paper, we present a systematic approach to developing robust control algorithms for a single-tendon shape memory alloy (SMA) bending actuator. Parameter estimation and uncertainty quantification are accomplished using Bayesian techniques. Specifically, we utilize Markov Chain Monte Carlo (MCMC) methods to estimate parameter uncertainty. The Bayesian parameter estimation results are used to construct a sliding mode control (SMC) algorithm where the bounds on uncertainty are used to guarantee controller robustness. The sliding mode controller utilizes the homogenized energy model (HEM) for SMA. The inverse HEM compensates for hysteresis and converts a reference bending angle to a reference temperature. Temperature in the SMA actuator is estimated using an observer, and the sliding mode controller ensures that the observer temperature tracks the reference temperature. The SMC is augmented with proportional-integral (PI) control on the bending angle error.}, booktitle={Proceedings of the ASME Conference on Smart Materials, Adaptive Structures and Intelligent Systems, vol 1}, author={Crews, J. H. and Smith, Ralph and Hannen, J. C.}, year={2013}, pages={391–400} }
 @article{crews_smith_pender_hannen_buckner_2012, title={Data-driven techniques to estimate parameters in the homogenized energy model for shape memory alloys}, volume={23}, ISSN={["1530-8138"]}, DOI={10.1177/1045389x12453965}, abstractNote={ The homogenized energy model is a unified framework for modeling hysteresis in ferroelectric, ferromagnetic, and ferroelastic materials. The homogenized energy model framework combines energy analysis at the lattice level with stochastic homogenization techniques, based on the assumption that quantities such as interaction and coercive fields are manifestations of underlying densities, to construct macroscopic material models. In this article, we focus on the homogenized energy model for shape memory alloys. Specifically, we develop techniques for estimating model parameters based on attributes of measured data. Both the local (mesoscopic) and macroscopic models are described, and the model parameters’ relationship to the material’s response is discussed. Using these relationships, techniques for estimating model parameters are presented. The techniques are applied to constant-temperature stress–strain and resistance–strain data. These estimates are used in two manners. In one method, the estimates are considered fixed and only the homogenized energy model density functions are optimized. For SMA, the HEM incorporates densities for the interaction and relative stress (the width of the hysteresis loop). In the second method, the estimates are included in the optimization algorithm. Both cases are compared to experimental data at various temperatures, and the optimized model parameters are compared to the initial estimates. }, number={17}, journal={JOURNAL OF INTELLIGENT MATERIAL SYSTEMS AND STRUCTURES}, author={Crews, John H. and Smith, Ralph C. and Pender, Kyle M. and Hannen, Jennifer C. and Buckner, Gregory D.}, year={2012}, month={Nov}, pages={1897–1920} }
 @inproceedings{crews_smith_2012, title={Density function optimization for the homogenized energy model of shape memory alloys}, DOI={10.1115/smasis2011-5036}, abstractNote={In this paper, we present two methods for optimizing the density functions in the homogenized energy model (HEM) of shape memory alloys (SMA). The density functions incorporate the polycrystalline behavior of SMA by accounting for material inhomogeneities and localized interaction effects. One method represents the underlying densities for the relative stress and interaction stress as log-normal and normal probability density functions, respectively. The optimal parameters in the underlying densities are found using a genetic algorithm. A second method represents the densities as a linear parameterization of log-normal and normal probability density functions. The optimization algorithm determines the optimal weights of the underlying densities. For both cases, the macroscopic model is integrated over the localized constitutive behavior using these densities. In addition, the estimation of model parameters using experimental data is described. Both optimized models accurately and efficiently quantify the SMA’s hysteretic dependence on stress and temperature, making the model suitable for use in real-time control algorithms.}, booktitle={Proceedings of the ASME Conference on Smart Materials, Adaptive Structures and Intelligent Systems (SMASIS 2011), vol 1}, author={Crews, J. H. and Smith, Ralph}, year={2012}, pages={371–379} }
 @article{crews_buckner_2012, title={Design optimization of a shape memory alloy-actuated robotic catheter}, volume={23}, ISSN={["1530-8138"]}, DOI={10.1177/1045389x12436738}, abstractNote={ In this article, we present a method for optimizing the design of a shape memory alloy–actuated robotic catheter. Highly maneuverable robotic catheters have the potential to revolutionize the treatment of cardiac diseases such as atrial fibrillation. To operate effectively, the catheter must navigate within the confined spaces of the heart, motivating the need for a tight bending radius. The design process is complicated by the shape memory alloy’s hysteretic relationships between strain, stress, and temperature. This article addresses the modeling and optimization of both a single-tendon and antagonistic tendon robotic catheter using COMSOL Multiphysics Modeling and Simulation software. Several design variables that affect the actuator behavior are considered; these include the shape memory alloy tendon radius and its prestrain, the shape memory alloy tendon offset from the neutral axis of the flexible beam, the flexible beam radius and elastic modulus, and the thermal boundary condition between the shape memory alloy tendon and the beam. A genetic algorithm is used to optimize the radius of curvature of the two catheter designs. Both a single-crystal and polycrystalline models are implemented in COMSOL and are experimentally validated. }, number={5}, journal={JOURNAL OF INTELLIGENT MATERIAL SYSTEMS AND STRUCTURES}, author={Crews, John H. and Buckner, Gregory D.}, year={2012}, month={Mar}, pages={545–562} }
 @article{crews_smith_2012, title={Modeling and Bayesian Parameter Estimation for Shape Memory Alloy Bending Actuators}, volume={8342}, ISSN={["1996-756X"]}, DOI={10.1117/12.914792}, abstractNote={In this paper, we employ a homogenized energy model (HEM) for shape memory alloy (SMA) bending actuators. Additionally, we utilize a Bayesian method for quantifying parameter uncertainty. The system consists of a SMA wire attached to a flexible beam. As the actuator is heated, the beam bends, providing endoscopic motion. The model parameters are fit to experimental data using an ordinary least-squares approach. The uncertainty in the fit model parameters is then quantified using Markov Chain Monte Carlo (MCMC) methods. The MCMC algorithm provides bounds on the parameters, which will ultimately be used in robust control algorithms. One purpose of the paper is to test the feasibility of the Random Walk Metropolis algorithm, the MCMC method used here.}, journal={BEHAVIOR AND MECHANICS OF MULTIFUNCTIONAL MATERIALS AND COMPOSITES 2012}, author={Crews, John H. and Smith, Ralph C.}, year={2012} }
 @inproceedings{crews_buckner_2012, title={Multi-objective design optimization of a shape memory alloy actuated robotic catheter}, DOI={10.1115/smasis2011-5037}, abstractNote={In this paper, we present a method for optimizing the design of a shape memory alloy (SMA) actuated robotic catheter. The robotic catheter is designed for use in endocardial ablation procedures, where “trackability” (bending flexibility) and “pushability” are desirable but conflicting catheter traits, leading to a multi-objective optimization problem. The catheter uses SMA tendons for internal actuation, which create a bending moment about a central structure. The design of SMA actuators is often non-intuitive and complicated due to the material’s hysteretic dependence on stress and temperature. The modeling and design difficulties increase when considering antagonistic SMA actuation, which is the case for the robotic catheter. The catheter is optimized using a genetic algorithm coupled with COMSOL Multiphysics Modeling and Simulation software. The objective functions are formulated in order to improve bending flexibility and pushability. Bending flexibility is quantified by radius of curvature. Pushability is a more subjective characteristic that depends on axial stiffness and friction, but for optimization purposes, it is quantified using axial stiffness and the surface area of the catheter. Several design variables that affect the catheter behavior are considered; these include the SMA tendon diameter and its pre-strain, the offset of the SMA tendon from the neutral axis of the central structure, and the central structure’s diameter and elastic modulus.}, booktitle={Proceedings of the ASME Conference on Smart Materials, Adaptive Structures and Intelligent Systems (SMASIS 2011), vol 1}, author={Crews, J. H. and Buckner, G. D.}, year={2012}, pages={381–389} }
 @article{furst_crews_seelecke_2012, title={Numerical and experimental analysis of inhomogeneities in SMA wires induced by thermal boundary conditions}, volume={24}, number={4-6}, journal={Continuum Mechanics and Thermodynamics}, author={Furst, S. J. and Crews, J. H. and Seelecke, S.}, year={2012}, pages={485–504} }
 @article{crews_mattson_buckner_2011, title={Multi-objective control optimization for semi-active vehicle suspensions}, volume={330}, ISSN={["1095-8568"]}, DOI={10.1016/j.jsv.2011.05.036}, abstractNote={In this paper we demonstrate a method for determining the optimality of control algorithms based on multiple performance objectives. While the approach is applicable to a broad range of dynamic systems, this paper focuses on the control of semi-active vehicle suspensions. The two performance objectives considered are ride quality, as measured by absorbed power, and thermal performance, as measured by power dissipated in the suspension damper. A multi-objective genetic algorithm (MOGA) is used to establish the limits of controller performance. To facilitate convergence, the MOGA is initialized with popular algorithms such as skyhook control, feedback linearization, and sliding mode control. The MOGA creates a Pareto frontier of solutions, providing a benchmark for assessing the performance of other controllers in terms of both objectives. Furthermore, the MOGA provides insight into the remaining achievable gains in performance.}, number={23}, journal={JOURNAL OF SOUND AND VIBRATION}, author={Crews, John H. and Mattson, Michael G. and Buckner, Gregory D.}, year={2011}, month={Nov}, pages={5502–5516} }
 @inproceedings{veeramani_crews_buckner_2009, title={Hysteretic recurrent neural networks: A tool for modeling hysteretic materials and systems}, volume={18}, number={7}, booktitle={Smart Materials & Structures}, author={Veeramani, A. S. and Crews, J. H. and Buckner, G. D.}, year={2009} }