journal article·2024-06

Grain refinement induced unusually large shape memory effect in lightweight titanium alloy

J.X. Zhang, J.Y. Zhang, W.C. Xiao, J.X. Hou, Q. Li, B. Xiao, S.Y. Yang, J.L. Wu, T.L. Zhang, T. Yang

Acta Materialia· Volume 272
Publisher page

Abstract

High-performance and lightweight shape memory alloys (SMAs) play a pivotal role in a variety of cutting-edge fields like aerospace, which however, are still quite difficult to be obtained until now. In the present work, we meticulously develop a novel lightweight Ti-12.5V-3.5Al-1Hf-0.8Fe-0.01B (at.%) SMA, which has a low mass density of ∼4.73 g/cm3. Prominently, distinct from the conventional wisdom in previous studies that is mainly based on eliminating grain boundaries or developing desired texture, we utilize the grain refinement strategy introduced by elaborate thermal-mechanical training, which effectively enables us to achieve exceptional shape memory response and strength-ductility combination simultaneously. It was revealed that a dominant β phase structure is achieved in this newly designed fine-grain SMA, accompanied by some α″ variants penetrating the grain boundaries. Compared to the coarse-grained counterpart, the deformation process becomes more intricate, involving stress-induced martensitic transformation and additional reorientations, which ultimately results in an unusually large recoverable strain up to 9.3 %. Furthermore, the fine-grain structure also contributes to an enhanced specific yield strength up to ∼159 MPa·m³/kg, while maintaining a large tensile ductility of ∼30 %. In light of its impressive structure-function integration and cost efficiency, this new-type SMA would emerge as a promising candidate for various advanced lightweight applications in a broad spectrum of fields.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Titanium Alloys Microstructure and Properties · Shape Memory Alloy Transformations · Advanced materials and composites

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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