Effect of the thermal processing on the microstructural, functional and mechanical properties of cast polycrystalline NiMnTi alloys
Source abstract
Abstract
The solid-state refrigeration and heat pumping field is gaining increasing attention as an alternative solution to the conventional vapour-based cooling devices. Among the shape memory alloys (SMAs), besides the reference NiTi system, outstanding elastocaloric performances are exhibited by the NiMnTi alloy. In this work, we produced two cast NiMnTi alloys which were characterized in terms of microstructural, structural, functional, and mechanical properties. Moreover, we assessed the effect of the heat treatments on the microstructure and on the physical properties of the produced alloys by calorimetric, structural and high-temperature microscope analyses. Furthermore, the heat treatments promoted partial mechanical cycling stability and a superelastic behavior without significant residual strains. Finally, the elastocaloric performance of the alloy was evaluated by direct adiabatic ΔT measurements upon elastocaloric cycles in four different loading conditions. The alloys exhibited promising elastocaloric performance for possible solid-state cooling applications. In particular, the best adiabatic ΔT value achieved in this work was 8.7 °C with a strain rate of 400 %/min and up to a strain of 5 % for the NiMnTi heat treated at 900°C. • All-d metal NiMnTi superelastic alloy exhibits promising elastocaloric effect. • Heat treatments tune NiMnTi microstructure and martensitic transition temperatures. • Heat treated cast NiMnTi shows a significant adiabatic temperature change of 8.7°C. • Cast NiMnTi can be applied in room temperature solid-state heating or cooling field. • Casting can be a low cost and effective method to produce elastocaloric NiMnTi.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Shape Memory Alloy Transformations · High Entropy Alloys Studies · Intermetallics and Advanced Alloy Properties
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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