Influence of Microstructure and Texture on Tensile Properties of an As-Rolled Ti2AlNb-Based Alloy
Bibliographic record
Abstract
Ti2AlNb-based alloys are widely used in aerospace applications due to their excellent high-temperature mechanical properties. This study aims to investigate the texture, microstructural evolution, and phase transformation behavior of Ti2AlNb-based alloy sheets during heat treatment and their effects on tensile properties. During heat treatment, B2 → O phase transformation occurs at 550 °C and 650 °C, while Ostwald ripening takes place at 700 °C and 850 °C. The α2 phase undergoes spheroidization around 1000 °C due to grain boundary separation and recrystallization. Additionally, the B2, O, and α2 phases all exhibit strong textures. The B2-phase texture follows a cubic orientation ({100}<001>), rotated ~30° around the normal direction (ND). The O-phase texture consists of a strong {100}<010> rolling texture and a weaker texture component <001>//RD, influenced by the B2-phase texture, rolling deformation, and variant selection during O-phase precipitation. Each B2 grain generates four variants, forming distinct O-phase textures within the same grain. The α2-phase texture exhibits typical rolling textures, [0001]//TD, <1¯21¯0>//ND, and {112¯0}<011¯0>, remaining stable after heat treatment. Tensile tests show that the rolled sheet has better ductility along the rolling direction (RD), while the transverse direction (TD) demonstrates higher yield strength (up to 1136 MPa). The anisotropy in tensile properties is mainly attributed to the O-phase texture, with minor contributions from the α2-phase and B2-phase textures. These findings provide a theoretical basis for optimizing the mechanical properties of Ti2AlNb-based alloys.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".