A simplified analytical model to simulate martensite reorientation and plasticity in shape memory alloy ring couplers

2021 ◽  
pp. 1045389X2110572
Author(s):  
Fabrizio Niccoli ◽  
Valentina Giovinco ◽  
Cedric Garion ◽  
Carmine Maletta ◽  
Paolo Chiggiato
Keyword(s):  
Shape Memory Alloy ◽  
Shape Memory ◽  
Analytical Model ◽  
High Vacuum ◽  
Ring Expansion ◽  
Particle Accelerators ◽  
European Organization ◽  
Elastic Plastic ◽  
Wide Range ◽  
Martensite Reorientation

Recent studies on Shape Memory Alloy rings have been undertaken at the European Organization for Nuclear Research (CERN) to develop smart and leak-tight couplers for Ultra High Vacuum systems of particle accelerators. A special thermo-mechanical process (training) is needed to provide SMA rings with proper functional properties, that is to allow thermal mounting, dismounting, and leak tight coupling within a given service temperature window. Low temperature ring expansion is a crucial part of the training process as it gives suitable size, shape recovery properties, and thermal stability range to the SMA element. An analytical model, based on simplified elastic-plastic axisymmetric concepts, has been developed and implemented in a commercial software to simulate isothermal SMA rings expansions. It is particularly useful to predict the final size of a martensitic SMA coupler as a function of the initial dimensions and of the pre-deformation parameters. The effectiveness of the model has been demonstrated by analyzing the stress/deformation field occurring in a wide range of ring geometries for different load cases including martensite reorientation and plasticity. The predictions of the analytical model have been systematically compared with those obtained by axisymmetric finite element (FE) analyses based on elastic-plastic constitutive models and experimental measurements.

scholarly journals Bio-inspired Miniature Suction Cups Actuated by Shape Memory Alloy

10.5772/7228 ◽  
2009 ◽  
Vol 6 (3) ◽  
pp. 29 ◽  
Author(s):  
Hu Bing-Shan ◽  
Wang Li-Wen ◽  
Fu Zhuang ◽  
Zhao Yan-zheng
Keyword(s):  
Shape Memory Alloy ◽  
Shape Memory ◽  
Analytical Model ◽  
Basic Structure ◽  
Thin Elastic Plate ◽  
Climbing Robots ◽  
Suction Cup ◽  
Sma Actuator ◽  
Suction Cups ◽  
Pressure Suction

Wall climbing robots using negative pressure suction always employ air pumps which have great noise and large volume. Two prototypes of bio-inspired miniature suction cup actuated by shape memory alloy (SMA) are designed based on studying characteristics of biologic suction apparatuses, and the suction cups in this paper can be used as adhesion mechanisms for miniature wall climbing robots without air pumps. The first prototype with a two-way shape memory effect (TWSME) extension TiNi spring imitates the piston structure of the stalked sucker; the second one actuated by a one way SMA actuator with a bias has a basic structure of stiff margin, guiding element, leader and elastic element. Analytical model of the second prototype is founded considering the constitutive model of the SMA actuator, the deflection of the thin elastic plate under compound load and the thermo-dynamic model of the sealed air cavity. Experiments are done to test their suction characteristics, and the analytical model of the second prototype is simulated on Matlab/simulink platform and validated by experiments.

Vibration Characteristics of Composite Plate Embedded with Shape Memory Alloy at Elevated Temperature

2013 ◽  
Vol 393 ◽  
pp. 655-660 ◽  
Author(s):  
Izzuddin Zaman ◽  
Bukhari Manshoor ◽  
Amir Khalid ◽  
Sherif Araby ◽  
Mohd Imran bin Ghazali
Keyword(s):  
Shape Memory Alloy ◽  
Shape Memory ◽  
Composite Structures ◽  
Composite Plate ◽  
Free Vibration Analysis ◽  
Composite Plates ◽  
Vibration Characteristics ◽  
Modal Testing ◽  
Phase Transformation Temperature ◽  
Wide Range

Unique functional material of shape memory alloy has attracted tremendous interest from researches, thus has been broadly investigated for a wide range application. Current research effort extends the use of SMA for the design of smart composite structures due to its shape memory effect, pseudo-elasticity and high damping capability. This paper presents an assessment of applications of the SMA materials for structural vibration controls, where the influences of SMA as reinforcement in the composite plate at different temperature are investigated. Four cases of composite plate are studied, which two of them are SMA-based composite fabricated at 0° and 45° angles, and the other two plates are neat (without SMA wires) and built with local stiffener. By using modal testing, the free vibration analysis is carried out to determine the vibration characteristics of composite plates. The results show that infusing SMA wires into composites increased the natural frequencies of the plate considerably, while decreased slightly for damping percentage. However, when SMA wires are heated, the damping percentage improved tremendously due to the phase transformation temperature of SMA from martensite to austenite. The outcome of this study reveals the potential of SMA materials in active vibration control.

scholarly journals Role of Glass Composition on Mechanical Properties of Shape Memory Alloy-Metallic Glass Composites

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Supat Chupradit ◽  
Indah Raya ◽  
Dinh Tran Ngoc Huy ◽  
Dmitry Bokov ◽  
Pham Van Tuan ◽  
...  
Keyword(s):  
Shape Memory Alloy ◽  
Shape Memory ◽  
Md Simulation ◽  
Shear Bands ◽  
Laminated Composite ◽  
Softening Effect ◽  
Glass Composites ◽  
Wide Range ◽  
Tension Loading

In this work, the molecular dynamics (MD) simulation was applied to design a laminated composite structure comprised of the shape memory alloy (SMA) and Cu-Zr metallic glasses (MGs). A wide range of MG compositions was considered to tune the mechanical features and improve the homogenous plastic deformation during the tension loading. The results indicated that the martensitic transformation in the SMA inhibited the sudden shear band propagation in the composite for all the samples. Moreover, it was revealed that the mechanism of plasticity was significantly affected by the change of MG composition. In the Cu-rich MGs, the formation and propagation of thick shear bands occurred at the end of the tension loading; however, the increase in Zr content induced the interaction of multiple shear bands with finer configurations in the system. Nevertheless, the excessive Zr addition in the MG composition facilitated the aggregation of nanopores at the interface of SMA and MGs, which may be due to the softening effect in the Zr-rich MGs. Finally, it is concluded that an optimized MG composition is required for the trade-off between the plasticity and the strength in the SMA-MG composites.

scholarly journals Machine Learning Enhanced Dynamic Response Modelling of Superelastic Shape Memory Alloy Wires

Materials ◽  
2022 ◽  
Vol 15 (1) ◽  
pp. 304
Author(s):  
Niklas Lenzen ◽  
Okyay Altay
Keyword(s):  
Machine Learning ◽  
Shape Memory Alloy ◽  
Strain Rate ◽  
Shape Memory ◽  
Thermodynamic Parameters ◽  
Expert Knowledge ◽  
Material Behavior ◽  
Model Parameters ◽  
Engineering Structures ◽  
Wide Range

Superelastic shape memory alloy (SMA) wires exhibit superb hysteretic energy dissipation and deformation capabilities. Therefore, they are increasingly used for the vibration control of civil engineering structures. The efficient design of SMA-based control devices requires accurate material models. However, the thermodynamically coupled SMA behavior is highly sensitive to strain rate. For an accurate modelling of the material behavior, a wide range of parameters needs to be determined by experiments, where the identification of thermodynamic parameters is particularly challenging due to required technical instruments and expert knowledge. For an efficient identification of thermodynamic parameters, this study proposes a machine-learning-based approach, which was specifically designed considering the dynamic SMA behavior. For this purpose, a feedforward artificial neural network (ANN) architecture was developed. For the generation of training data, a macroscopic constitutive SMA model was adapted considering strain rate effects. After training, the ANN can identify the searched model parameters from cyclic tensile stress–strain tests. The proposed approach is applied on superelastic SMA wires and validated by experiments.

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