The Effect of Heat Treatments on Microstructure and Creep Properties of Powder Metallurgy Beta Gamma Titanium Aluminide Alloys
Bibliographic record
Abstract
The microstructure and creep properties of two powder metallurgy (PM) ‘beta gamma’ titanium aluminide alloys are presented. Alloy powders with nominal compositions of TiAl-4Nb-3Mn (G1) and TiAl-2Nb-2Mo (G2) were produced by gas atomization and consolidated by a two-step hot isostatic pressing (HIP) process (1250 °C/200 MPa/1 hour + 1100 °C/200 MPa/3 hours + slow cooling to room temperature). After HIP, the materials were given a step cooled heat treatment (SCHT) of 40 min at 1400 °C, furnace cooling to 1280 °C, and air cooling to room temperature. Selected specimens were aged at 900 °C for 6 or 24 hours. The SCHT yielded similar fully lamellar microstructures for both alloys, with a lamellar spacing of 0.04 m, but with different grain sizes averaging 80 m (G1) and 40 m (G2). The aging treatments generated precipitates along lamellar colony boundaries in both alloys, but along lamellar interfaces only in alloy G2. Constant load tensile creep tests were performed at 760 °C and 276 MPa. Alloy G2 exhibited superior creep performance compared to alloy G1, due to the quantity and size of precipitate particles at the lamellar interfaces.
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".