Embedded shape memory alloys confer aerodynamic profile adaptivity

Additive manufacturing of NiTi shape memory alloy and its industrial applications

NiTi shape memory alloys are the prime choice for many engineering and biomedical applications due to their unique response to environmental/external stimuli. The capability of laser powder bed fusion (LPBF) to additively manufacture high-quality density NiTi alloy with intricate geomaterial configuration, good surface quality, and chemical homogeneity makes the LPBF process the preferred choice among other additive manufacturing (AM) methods for manufacturing the NiTi alloy. The AM process parameters have a decisive effect on the functional and mechanical properties of NiTi alloy. There is a need to understand the resultant effect of the interrelationship between the process parameters on the final NiTi additive manufactured structures. The inherent high rate of melting and cooling of the LPBF process resulting in high internal stress could cause adverse effects such as cracks and Ni-loss which are detrimental to the phase transformation temperature of the NiTi alloy. Despite the current challenges, the literature reveals that LPBF NiTi components demonstrated functional and mechanical properties according to the ASTM standard and have been used widely for biomedical applications due to its stress-strain hysteresis, which is similar to bone tissues. The alloy is also used extensively for high-value engineering applications such as the automotive and aerospace industries due to its actuation properties.

Shape Memory Alloys for Aerospace, Recent Developments, and New Applications: A Short Review

Shape memory alloys (SMAs) show a particular behavior that is the ability to recuperate the original shape while heating above specific critical temperatures (shape memory effect) or to withstand high deformations recoverable while unloading (pseudoelasticity). In many cases the SMAs play the actuator’s role. Starting from the origin of the shape memory effect, the mechanical properties of these alloys are illustrated. This paper presents a review of SMAs applications in the aerospace field with particular emphasis on morphing wings (experimental and modeling), tailoring of the orientation and inlet geometry of many propulsion system, variable geometry chevron for thrust and noise optimization, and more in general reduction of power consumption. Space applications are described too: to isolate the micro-vibrations, for low-shock release devices and self-deployable solar sails. Novel configurations and devices are highlighted too.

Sensitivity and Uncertainty Analysis of One-Dimensional Tanaka and Liang-Rogers Shape Memory Alloy Constitutive Models

A shape memory alloy (SMA) can remember its original shape and recover from strain due to loading once it is exposed to heat (shape memory effect). SMAs also exhibit elastic response to applied stress above the characteristic temperature at which transformation to austenite is completed (pseudoelasticity or superelasticity). Shape memory effect and pseudoelasticity of SMAs have been addressed by several microscopic thermodynamic and macroscopic phenomenological models using different modeling approaches. The Tanaka and Liang-Rogers models are two of the most widely used macroscopic phenomenological constitutive models for describing SMA behavior. In this paper, we performed sensitivity and uncertainty analysis using Sobol and extended Fourier Amplitude Sensitivity Testing (eFAST) methods for the Tanaka and Liang-Rogers models at different operating temperatures and loading conditions. The stress-dependent and average sensitivity indices have been analyzed and are presented for determining the most influential parameters for these models. The results show that variability is primarily caused by a change in operating temperature and loading conditions. Both models appear to be influenced by the uncertainty in elastic modulus of the material significantly. The analyses presented in this paper aim to provide a better insight for designing applications using SMAs by increasing the understanding of these models’ sensitivity to the input parameters and the cause of output variability due to uncertainty in the same input parameters.