A macroscopic multi-mechanism based constitutive model for the thermo-mechanical cyclic degeneration of shape memory effect of NiTi shape memory alloy

Advances in enhancing structural and functional fatigue resistance of superelastic NiTi shape memory alloy: A Review

“…This uncontrolled higher temperature causes unpredictable structural and functional failures (Usoro et al, 2012). Investigations on NiTi under cyclic thermomechanical loading shows the change in plastic strain, fracture mode and fatigue life with an increase in the maximum heating temperature (Hornbogen, 2004;Yu et al, 2017). Considering this need for overheating in applications, Qin et al (2019) investigated the structural and functional fatigue behaviour of Ni 49.8 Ti 50.2 (A f = 73°C) wires under different maximum heating temperatures (T max ).…”

A Temperature-Dependent Model of Shape Memory Alloys Considering Tensile-Compressive Asymmetry and the Ratcheting Effect

“…The ratcheting deformation of SMAs during cycling has been widely investigated experimentally by researchers in the last decade, including strain-controlled [ 36 , 37 , 38 , 39 ] and stress-controlled [ 17 , 40 , 41 , 42 , 43 ] cyclic loading tests. According to the experimental observations, several phenomenological constitutive models for SMAs have been developed to successfully predict ratcheting deformation during multiple cycles [ 44 , 45 , 46 , 47 , 48 , 49 , 50 ]. Tanaka et al [ 44 ] introduced three internal variables into a macroscopic theoretical framework to interpret the hysteresis behavior of SMAs during thermomechanical cyclic loadings, and they also analyzed the subloops due to the incomplete transformations of the SMAs.…”

“…Saint-Sulpice et al [ 45 ] developed the constitutive equations of a novel 3D macroscopic model for SMAs which could reproduce all the experimental observations, in which a permanent inelastic strain occurs and increases during cyclic tests. On the basis of Kang’s experimental observations [ 17 ] for both pseudo-elastic and shape-memory SMAs, Kan and Kang [ 46 ] and Yu et al [ 47 ] constructed the corresponding constitutive models to predict uniaxial transformation ratcheting deformation of pseudo-elastic and shape-memory SMAs, respectively. Xiao et al [ 48 ] considered the effects of loading rate on the results of cyclic tests and presented a 3D thermomechanically coupled constitutive model to describe the rate-dependent cyclic performance of pseudo-elastic SMAs, in which the ratcheting deformation was included.…”

Uncertainty quantification of the parameters and predictions of a phenomenological constitutive model for thermally induced phase transformation in Ni–Ti shape memory alloys

“…Nickel-titanium (Ni-Ti) alloys are one of the most common shape memory alloys (SMAs), which are widely used in different engineering applications, such as biomedical devices and implants, microelectromechanical systems, sensors and actuators, seismic protection tools, and aerospace products and structures [1,2]. Such applications are enabled by either the shape memory effect or super-elasticity, which in turn are the result of a reversible thermoelastic martensitic transformation that is triggered by applying thermal and/or mechanical loads [3,4]. The macroscopic thermal actuation response of SMAs enables actuators with high specific weight ratio compared to the conventional alternatives [5,6].…”

“…In regard to the structure-property linkages, a number of constitutive models have been proposed over the recent decades to predict the thermo-mechanical response of Ni-Ti SMAs. These models can be categorized into phenomenological [16][17][18][19][20][21][22][23][24][25] or micromechanical-based models [7,[26][27][28][29] which have been reviewed thoroughly by Patoor et al [8] and Lagoudas et al [30]. Generally, micro-mechanical models are high-fidelity and expensive models that use microstructural information to predict the macroscopic behavior of SMAs while phenomenological models are cheap and fast since the free energy associated with a non-microscopic homogenized material volume is taken into account for the prediction of the macroscopic responses [21].…”