Constitutive Modelling for Restrained Recovery of Shape Memory Alloys Based on Artificial Neural Network

A key property for the design of new shape memory alloys is their working temperature range that depends on their transformation temperature T0. In previous works, T0 was predicted using a simple linear regression with respect to the energy difference between the parent and the martensitic phases, [Formula: see text]E[Formula: see text]. In this paper, we developed an accurate method to predict T0 based on machine learning assisted by the first-principles calculations. First-principles calculations were performed on 15 shape memory alloys; then, we proposed an artificial neural network method that used not only computed [Formula: see text]E[Formula: see text] but also bulk moduli as input variables to predict T0. The prediction error of T0 was improved to 49 K for the proposed artificial neural network compared with 188 K for simple linear regression.

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Rapid Characterization of Shape Memory Alloy Material Parameters Using Computational Intelligence Methods

Volume 1: Development and Characterization of Multifunctional Materials; Modeling, Simulation and Control of Adaptive Systems; Integrated System Design and Implementation ◽

10.1115/smasis2013-3016 ◽

2013 ◽

Author(s):

James V. Henrickson ◽

Kenton Kirkpatrick ◽

John Valasek

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Shape Memory Alloy ◽

Shape Memory Alloys ◽

Shape Memory ◽

Training Data ◽

Material Parameters ◽

Influence Coefficients ◽

Alloy Material ◽

Artificial Neural

Shape memory alloys are capable of delivering advantageous solutions to a wide range of engineering-based problems. Implementation of these solutions, however, is often complicated by the hysteretic, non-linear, thermo-mechanical behavior of the material. Although existing shape memory alloy constitutive models are largely accurate in describing this unique behavior, they require prior characterization of the material parameters. Consequently, before thorough modeling and simulation can occur for a shape memory alloy-based project, one must first go through the process of identifying several material parameters unique to shape memory alloys. Current characterization procedures necessitate extensive experimentation, data collection, and data processing. As a result, these methods simultaneously create a high barrier of entry for engineers new to active materials and impede the advanced study of shape memory alloy material parameter evolution. This paper develops a novel method in which computational intelligence methods are used to rapidly identify shape memory alloy material parameters. Specifically, an artificial neural network is trained to identify transformation temperatures and stress influence coefficients of given shape memory alloy specimens using strain-temperature coordinates as inputs. After generating training data through the use of a constitutive model, the resulting trained artificial neural network was used to identify parameters for a number of randomly generated theoretical shape memory alloys. Results presented in the paper show that the artificial neural network was able to rapidly identify both transformation temperatures and stress influence coefficients with satisfactory accuracy. The generation of training data was then repeated using Taguchi methods. Further results presented in the paper show that the artificial neural network trained with the Taguchi-based training data yielded improved characterization accuracy while using less training data.

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Hysteresis Identification of Shape Memory Alloy Actuators Using a Novel Artificial Neural Network Based Presiach Model

ASME 2010 Conference on Smart Materials, Adaptive Structures and Intelligent Systems, Volume 1 ◽

10.1115/smasis2010-3903 ◽

2010 ◽

Cited By ~ 2

Author(s):

Mohammad R. Zakerzadeh ◽

Mohsen Firouzi ◽

Hassan Sayyaadi ◽

Saeed Bagheri Shouraki

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Performance Evaluation ◽

Shape Memory Alloy ◽

Shape Memory ◽

Preisach Model ◽

Hysteresis Behavior ◽

Good Ability ◽

Experimental Apparatus ◽

Artificial Neural

In systems with hysteresis behavior like Shape Memory Alloy (SMA) actuators and Piezo actuators, an accurate modeling of hysteresis behavior either for performance evaluation and identification or controller design is essentially needed. One of the most interesting hysteresis none-linearity identification methods is Preisach model which the hysteresis is modeled by linear combination of hysteresis operators. In spite of good ability of the Preisach model to extract the main features of system with hysteresis behavior, due to its numerical nature, it is not convenient to use in real time control applications. In this paper a novel artificial neural network (ANN) approach based on the Preisach model is presented which provides accurate hysteresis none-linearity modeling. It is shown that the proposed approach can represent hysteresis behavior more accurately in compare with the classical Preisach model and can be used for many applications such as hysteresis non-linearity control, hysteresis identification and realization for performance evaluation in some physical systems such as magnetic and SMA materials. It is also greatly decrease the extremely large amount of calculation needed to numerically implement the Preisach hysteresis model. For evaluation of the proposed approach an experimental apparatus consists of one-dimensional flexible aluminum beam actuated with a SMA wire is used. It is shown that the proposed ANN based Preisach model can identify hysteresis none-linearity more accurately than the classical Preisach model besides to its reduction in the simulation and computation time.

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Artificial neural network models to predict the response of 55NiTi shape memory alloy under stress and thermal cycles

Neural Computing and Applications ◽

10.1007/s00521-021-06643-x ◽

2021 ◽

Author(s):

J. S. Owusu-Danquah ◽

Abdallah Bseiso ◽

Srinivas Allena

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Shape Memory Alloy ◽

Shape Memory ◽

Network Models ◽

Thermal Cycles ◽

Neural Network Models ◽

Artificial Neural ◽

Artificial Neural Network Models

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Constitutive Modelling of INCONEL 718 using Artificial Neural Network

IOP Conference Series Materials Science and Engineering ◽

10.1088/1757-899x/225/1/012054 ◽

2017 ◽

Vol 225 ◽

pp. 012054

Author(s):

Olufunminiyi Abiriand Bhekisipho Twala

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Inconel 718 ◽

Constitutive Modelling ◽

Artificial Neural

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Determination of Angular Position in a Shape Memory Alloy Based Rotary Manipulator Using an Artificial Neural Network

Volume 1: Development and Characterization of Multifunctional Materials; Modeling, Simulation and Control of Adaptive Systems; Structural Health Monitoring; Keynote Presentation ◽

10.1115/smasis2014-7421 ◽

2014 ◽

Cited By ~ 2

Author(s):

Pavanesh Narayanan ◽

Mohammad Elahinia

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Shape Memory ◽

Angular Position ◽

Ann Model ◽

Resistance Change ◽

Hysteresis Behavior ◽

Heating And Cooling ◽

Original Shape ◽

Artificial Neural

Shape memory alloys (SMA) when subjected to deformation at low temperature can recover their original shape by heating above a temperature called Austenite transformation temperature. This original shape is sustained till the material is deformed again by an applied stress. This property makes the SMA a unique actuator, which doesn’t require any other components. Also, the material’s resistance changes with deformation. Thus the change in resistance can be used to sense the deformation, which eliminates the requirements of additional sensors. This can make the system more compact and reduce the cost. In our study, a binary Nickel-Titanium alloy is used as a rotary actuator. The actuation is controlled by adjusting the temperature through controlled joule heating by varying the electric current. The manipulator used in this research is a single degree-of-freedom, bias type actuator. SMA actuation in this system is under a varying stress, thereby creating a complex thermo-mechanical condition which affects the transformation temperatures, significantly. Also, the resistance change during heating and cooling paths exhibit hysteresis behavior. This paper investigates the use of artificial neural network (ANN) in establishing relationship between resistance and angular position of the manipulator. To model the hysteresis behavior of the SMA, in addition to resistance of the SMA, other electric properties like voltage etc are given as input to the ANN. The obtained ANN model is able to determine the angular position of the rotary manipulator with good accuracy.

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