ω-Strengthened Ti-23Nb alloy with twinning-induced plasticity developed via reverse martensitic transformation
Source abstract
Abstract
The twinning-induced plasticity (TWIP) effect contributes to high strength and ductility synergy in metastable β Ti alloys. Moreover, stress-induced α″ martensites (SIMs) are usually concurrent with TWIP, resulting in low yield strength. As a new pathway, the production of ω-strengthened metastable β Ti alloys from full α″ Ti alloys can be accomplished via reverse martensitic transformation. However, the deformation mechanisms of the metastable β Ti alloys acquired by this pathway have not been adequately reported. Accordingly, in this study, the full α″-type solution-treated (ST) samples of Ti-23Nb at.% alloy were annealed at 573 K followed by slow cooling to acquire the air-cooled (AC) and furnace-cooled (FC) samples with β phase and ω precipitates. Mechanical properties and deformation behaviors of these samples were systematically investigated. The AC and FC samples exhibited higher yield strengths than the ST samples. Although SIMs still existed in the AC samples, they were completely suppressed in the FC samples. The annealed samples demonstrated high ductility due to the TWIP effects stemming from {332}<113> β twins. Furthermore, a distorted α″ phase exhibiting the abnormal orientation relationships (ORs) < 1 1 ¯ 0 > β//<001> α″, {332} β//{100} α″, and {110} β//{110} α″ with both β matrix and twin was revealed by high-resolution transmission electron microscopy and crystallographic analyses. These ORs were governed by the lattice distortion associated with a unique β matrix → distorted α″ → {332} β twin pathway. This study suggests that metastable β Ti alloys with enhanced mechanical properties can be achieved via the α″ → β reverse martensitic transformation and ω precipitation.
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Research context
OpenAlex topics: Titanium Alloys Microstructure and Properties · Intermetallics and Advanced Alloy Properties · Advanced Welding Techniques Analysis
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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