Mesoscale Analysis of Porous Shape Memory Alloys

2000 ◽  
Author(s):  
Virginia G. DeGiorgi ◽  
Muhammad A. Qidwai
Keyword(s):  
Shape Memory Alloys ◽  
Shape Memory ◽  
Dynamic Loading ◽  
Smart Materials ◽  
Bulk Material ◽  
Material Behavior ◽  
Loading Conditions ◽  
Shape Variation ◽  
Energy Absorbing ◽  
Dynamic Loading Conditions

Abstract Shape memory alloys are frequently used in smart materials and structures as the active component. Their ability to provide high force and large displacements has been used to the advantage in many applications. The majority of applications to date utilize solid shape memory alloy materials in quasi-static loading conditions. Recent work has proposed the use of porous SMAs as an energy absorbing material under dynamic loading conditions. The use of porous SMAs under dynamic loading will require advancements in the understanding of SMA behavior both in the dense or solid form and in the porous form. The current work examines the quasi-static behavior of porous SMA as a first step. The material behavior is modeled on a mesoscale level allowing for the examination of pore size and shape variation effects. Bulk material response is estimated and compared with micromechanical periodic unit cell predictions.

Effects of Annealing on Microstructure and Martensitic Transition of Ni-Mn-Co-In Ferromagnetic Shape Memory Alloys

2011 ◽  
Vol 674 ◽  
pp. 171-175
Author(s):  
Katarzyna Bałdys ◽  
Grzegorz Dercz ◽  
Łukasz Madej
Keyword(s):  
Electron Microscopy ◽  
Shape Memory Alloys ◽  
Shape Memory ◽  
Smart Materials ◽  
Martensitic Transition ◽  
Magnetic Shape Memory ◽  
Ferromagnetic Shape Memory Alloys ◽  
Initial State ◽  
X Ray ◽  
Ferromagnetic Shape Memory

The ferromagnetic shape memory alloys (FSMA) are relatively the brand new smart materials group. The most interesting issue connected with FSMA is magnetic shape memory, which gives a possibility to achieve relatively high strain (over 8%) caused by magnetic field. In this paper the effect of annealing on the microstructure and martensitic transition on Ni-Mn-Co-In ferromagnetic shape memory alloy has been studied. The alloy was prepared by melting of 99,98% pure Ni, 99,98% pure Mn, 99,98% pure Co, 99,99% pure In. The chemical composition, its homogeneity and the alloy microstructure were characterized using scanning electron microscopy (SEM) and transmission electron microscopy (TEM). The phase composition was also studied by X-ray analysis. The transformation course and characteristic temperatures were determined by the use of differential scanning calorimetry (DSC) and magnetic balance techniques. The results show that Tc of the annealed sample was found to decrease with increasing the annealing temperature. The Ms and Af increases with increasing annealing temperatures and showed best results in 1173K. The studied alloy exhibits a martensitic transformation from a L21 austenite to a martensite phase with a 7-layer (14M) and 5-layer (10M) modulated structure. The lattice constants of the L21 (a0) structure determined by TEM and X-ray analysis in this alloy were a0=0,4866. The TEM observation exhibit that the studied alloy in initial state has bigger accumulations of 10M and 14M structures as opposed from the annealed state.

Adaptive Elastic SMA Elements for Damping Applications

2013 ◽  
Author(s):  
Alexander Czechowicz ◽  
Peter Dültgen ◽  
Sven Langbein
Keyword(s):  
Boundary Conditions ◽  
Shape Memory Alloys ◽  
Shape Memory ◽  
Smart Materials ◽  
Electrical Resistance ◽  
Thermal Activation ◽  
Hysteretic Behavior ◽  
Damping Function ◽  
Actual Shape ◽  
Elastic Elements

Shape memory alloys (SMA) are smart materials, which have two technical usable effects: While pseudoplastic SMA have the ability to change into a previously imprinted actual shape through the means of thermal activation, pseudoelastic SMA show a reversible mechanical elongation up to 8% at constant temperature. The transformation between the two possible material phases (austenite and martensite) shows a hysteretic behavior. As a result of these properties, SMA can be used as elastic elements with intrinsic damping function. Additionally the electrical resistance changes remarkably during the material deformation. These effects are presented in the publication in combination with potential for applications in different branches at varying boundary conditions. The focus of the presented research is concentrated on the application of elastic elements with adaptive damping function. As a proof for the potential considerations, an application example sums up this presentation.

Development and Verification of a Dynamic TKR Model

2002 ◽  
Author(s):  
Jason P. Halloran ◽  
Anthony J. Petrella ◽  
Paul J. Rullkoetter
Keyword(s):  
Finite Element ◽  
Contact Mechanics ◽  
Dynamic Loading ◽  
Soft Tissues ◽  
Dynamic Models ◽  
Total Joint Replacement ◽  
Fe Model ◽  
Loading Conditions ◽  
Dynamic Loading Conditions

The success of current total knee replacement (TKR) devices is contingent on the kinematics and contact mechanics during in vivo activity. Indicators of potential clinical performance of total joint replacement devices include contact stress and area due to articulations, and tibio-femoral and patello-femoral kinematics. An effective way of evaluating these parameters during the design phase or before clinical use is via computationally efficient computer models. Previous finite element (FE) knee models have generally been used to determine contact stresses and/or areas during static or quasi-static loading conditions. The majority of knee models intended to predict relative kinematics have not been able to determine contact mechanics simultaneously. Recently, however, explicit dynamic finite element methods have been used to develop dynamic models of TKR able to efficiently determine joint and contact mechanics during dynamic loading conditions [1,2]. The objective of this research was to develop and validate an explicit FE model of a TKR which includes tibio-femoral and patello-femoral articulations and surrounding soft tissues. The six degree-of-freedom kinematics, kinetics and polyethylene contact mechanics during dynamic loading conditions were then predicted during gait simulation.

Modeling Smart Materials Using Hysteretic Recurrent Neural Networks

2009 ◽  
Author(s):  
Arun Veeramani ◽  
John Crews ◽  
Gregory D. Buckner
Keyword(s):  
Shape Memory Alloys ◽  
Shape Memory ◽  
Smart Materials ◽  
Real Time Control ◽  
Ferromagnetic Shape Memory Alloys ◽  
Two Phase ◽  
Time Control ◽  
Novel Approach ◽  
Three Phase ◽  
Ferromagnetic Shape Memory

This paper describes a novel approach to modeling hysteresis using a Hysteretic Recurrent Neural Network (HRNN). The HRNN utilizes weighted recurrent neurons, each composed of conjoined sigmoid activation functions to capture the directional dependencies typical of hysteretic smart materials (piezoelectrics, ferromagnetic, shape memory alloys, etc.) Network weights are included on the output layer to facilitate training and provide statistical model information such as phase fraction probabilities. This paper demonstrates HRNN-based modeling of two- and three-phase transformations in hysteretic materials (shape memory alloys) with experimental validation. A two-phase network is constructed to model the displacement characteristics of a shape memory alloy (SMA) wire under constant stress. To capture the more general thermo-mechanical behavior of SMAs, a three-phase HRNN model (which accounts for detwinned Martensite, twinned Martensite, and Austensite phases) is developed and experimentally validated. The HRNN modeling approach described in this paper readily lends itself to other hysteretic materials and may be used for developing real-time control algorithms.

Shape memory alloy–actuated prestressed composites with application to morphing automotive fender skirts

2018 ◽  
Vol 30 (3) ◽  
pp. 479-494 ◽  
Author(s):  
Venkata Siva C Chillara ◽  
Leon M Headings ◽  
Ryohei Tsuruta ◽  
Eiji Itakura ◽  
Umesh Gandhi ◽  
...  
Keyword(s):  
Shape Memory Alloy ◽  
Shape Memory Alloys ◽  
Shape Memory ◽  
Smart Materials ◽  
Design Procedure ◽  
Building Blocks ◽  
Smart Composite ◽  
Thermally Induced ◽  
One Dimensional ◽  
Flat Shape

This work presents smart laminated composites that enable morphing vehicle structures. Morphing panels can be effective for drag reduction, for example, adaptive fender skirts. Mechanical prestress provides tailored curvature in composites without the drawbacks of thermally induced residual stress. When driven by smart materials such as shape memory alloys, mechanically-prestressed composites can serve as building blocks for morphing structures. An analytical energy-based model is presented to calculate the curved shape of a composite as a function of force applied by an embedded actuator. Shape transition is modeled by providing the actuation force as an input to a one-dimensional thermomechanical constitutive model of a shape memory alloy wire. A design procedure, based on the analytical model, is presented for morphing fender skirts comprising radially configured smart composite elements. A half-scale fender skirt for a compact passenger car is designed, fabricated, and tested. The demonstrator has a domed unactuated shape and morphs to a flat shape when actuated using shape memory alloys. Rapid actuation is demonstrated by coupling shape memory alloys with integrated quick-release latches; the latches reduce actuation time by 95%. The demonstrator is 62% lighter than an equivalent dome-shaped steel fender skirt.

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