The effect of microstructure engineering on the pseudoelastic and elastocaloric enhancement of a Ni-rich Ti–Ni alloy

The elastocaloric effect, which utilizes pseudoelastic NiTi alloys, has recently gained significant attention due to its substantial energy-saving potential, negligible environmental impact, and cost-effectiveness. Notably, significant elastocaloric effects (eCE) arising from the stress-induced B2↔B19’ transition under axial load have been demonstrated in NiTi shape memory alloys. This study investigates the correlation of microstructure - cold rolling, and aging processes on the elastocaloric properties. The microstructure of a 30% cold rolled Ni-rich NiTi alloy, followed by aging at 500°C for 2h, was examined through high-resolution transmission electron microscopy (HR-TEM). It is found that the synergistic interplay between the nanocrystalline structure and coherent Ni-rich nanoprecipitates provides a strong elastocaloric response, yielding a noteworthy adiabatic temperature change (ΔT ad ) of 28.5 K, (ΔT ad cooling) of 17 K, and a recoverable strain of 97% even after ten cycles at a strain of 9%. Digital image correlation (DIC) study shows the formation and propagation of Lüders-like bands across the middle, upper, and lower gauge length regions of the specimen during the load-unload test. The results revealed a partially uniform strain distribution of approximately 0.045 along the gauge length. The DIC analysis and microstructural study demonstrated that single-oriented strain bands with the lower Strain Localization Factor (SLF), because of fine grains and uniform cohesive interface between matrix and Ni 4 Ti 3 precipitates, facilitated more effective reverse transformation and generated a greater elastocaloric response compared to crossed strain bands. A key challenge in NiTi elastocaloric research lies in understanding how processing conditions influence microstructure, strain distribution, and functional behavior. In this study, we address this gap by systematically correlating thermomechanical treatments, DIC-based strain analysis, and pseudoelastic and elastocaloric responses to identify optimal conditions and improve performance predictability.