Numerical modeling of shape memory alloy plates considering tension/compression asymmetry and its verification under pure bending

Kim, Young‐Jin; Lee, Chang-Ho; Kim, Joung-Hun; Lim, Jae Hyuk

doi:10.1016/j.ijsolstr.2017.12.004

Cited by 15 publications

(8 citation statements)

References 28 publications

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“…Furthermore, friction and local deformation beneath the contact points can also affect the results [45]. To convert the measured output from these tests (punch displacement, y p and punch force, F p ) into stress-strain (σ-ε) response, inverse fitting models are usually used in literature [46][47][48]. These methods require accurate modeling of the test with a predetermined hardening model and costly optimization loops.…”

Section: Problem (A)-materials Characterizationmentioning

confidence: 99%

Application of Machine Learning to Bending Processes and Material Identification

Cruz

Barbosa

Santos

et al. 2021

Metals

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The increasing availability of data, which becomes a continually increasing trend in multiple fields of application, has given machine learning approaches a renewed interest in recent years. Accordingly, manufacturing processes and sheet metal forming follow such directions, having in mind the efficiency and control of the many parameters involved, in processing and material characterization. In this article, two applications are considered to explore the capability of machine learning modeling through shallow artificial neural networks (ANN). One consists of developing an ANN to identify the constitutive model parameters of a material using the force–displacement curves obtained with a standard bending test. The second one concentrates on the springback problem in sheet metal press-brake air bending, with the objective of predicting the punch displacement required to attain a desired bending angle, including additional information of the springback angle. The required data for designing the ANN solutions are collected from numerical simulation using finite element methodology (FEM), which in turn was validated by experiments.

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Section: Problem (A)-materials Characterizationmentioning

confidence: 99%

Application of Machine Learning to Bending Processes and Material Identification

Cruz

Barbosa

Santos

et al. 2021

Metals

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“…Three new parameters are incorporated into the suggested transformation kinetics, and the suggested transformation kinetics can predict the thermo-mechanical behaviour of SMAs more precisely under compression. Kim et al (2018) developed a numerical model of an SMA plate reflecting the tension–compression asymmetry of axial stress under pure bending. To achieve the goal, the Brinson model with modified martensite transformation kinetics was proposed with the Drucker-Prager yield criterion to realize the tension–compression symmetry of SMA.…”

Section: Introductionmentioning

confidence: 99%

A phenomenological constitutive model for one-dimensional shape memory alloys based on artificial neural network

Wang

2021

Journal of Intelligent Material Systems and Structures

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Shape Memory Alloys (SMA) have become a material with great application prospects because of their unique characteristics and superior properties. A phenomenological constitutive model for SMA is constructed in the current paper. The proposed constitutive model is based on the phenomena observed in the experiment, and Artificial Neural Networks (ANN) are used to simulate part of the characteristics of SMA. The parameter identification method is also proposed, where Back-Propagation (BP) algorithm and the nonlinear optimisation algorithm are used at the same time. The numerical experiment has been carried out, which can well capture the constitutive relationship curve obtained from uniaxial tension and compression experiments of SMA, thus the model can be verified. The model can also describe the phase transformation characteristics of SMA well.

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“…Further, the article presents computational algorithms to implement these approaches in the MATLAB interface as the software provides an ease to the user to adopt any constitutive model for SMA. However, the same is not true for commercial FEM software such as ANSYS, MARC, ABAQUS, etc which are widely used to model SMA material [28][29][30]. Such software offers limited constitutive models for simulating the behavior of SMA material.…”

Section: Introductionmentioning

confidence: 99%

Comparative analysis of numerical methods for constitutive modeling of shape memory alloys

Bhatt¹,

Soni²,

Singla³

2021

Modelling Simul. Mater. Sci. Eng.

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This article presents a comparative analysis of four numerical approaches—Newton Raphson method, trust region dogleg method, implicit solver (IS) based method considering a proposed simplified differential phase kinetics as well as an existing arbitrary loading based phase kinetics to solve the constitutive equation of shape memory alloy (SMA). These numerical methods can be applied to model any SMA-based actuator under thermo-mechanical loading. To demonstrate these methods, a configuration of SMA wire with a normal bias spring is considered as a case study, and its mathematical model is solved with the above-mentioned numerical approaches. Further, a comparative study is carried out that compares the solutions obtained via these methods and with the published literature. It highlights the numerical stability, computational time, and the level of complexity for the implementation of the numerical methods. The study details out both the merits and demerits of the presented numerical methods and the computational algorithms to implement these methods. Among the discussed methods, it is concluded that the IS-based numerical method with the proposed differential form of Chung’s phase kinetics model seems better for implementing the constitutive modeling for the selected case study.

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Numerical modeling of shape memory alloy plates considering tension/compression asymmetry and its verification under pure bending

Cited by 15 publications

References 28 publications

Application of Machine Learning to Bending Processes and Material Identification

Application of Machine Learning to Bending Processes and Material Identification

A phenomenological constitutive model for one-dimensional shape memory alloys based on artificial neural network

Comparative analysis of numerical methods for constitutive modeling of shape memory alloys

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