Improving thermal stability and creep resistance by Sc addition in near-α high-temperature titanium alloy
Bibliographic record
Abstract
High-temperature titanium alloys’ thermal stability and creep resistance are significant during service in high temperatures. This study systematically investigated the thermal stability and mechanical properties of Ti-6.5A1–2.5Sn-9Zr-0.5Mo-1Nb-1W-0.3Si- xSc ( x , 0–0.5 wt.%) at 650 °C. The lamellar secondary α phase is refined and the formation of Sc 2 O 3 is increased with the increasing scandium ( Sc ) additions, which improves the strength of the alloy, while excessive Sc 2 O 3 becomes the crack source and deteriorates the plasticity. The oxygen content in the matrix is reduced by the interaction between Sc and oxygen, inhibiting the growth of the Ti 3 Al phase and improving the thermal stability of the alloy. Meanwhile, Sc accelerates the dissolution of the residual β phase and precipitation of fine, diffusely distributed ellipsoidal silicides, which strongly prevents dislocation movement. The enhancement of creep resistance for the Sc -containing alloy is attributed to the refined lamellar secondary α phases, Sc 2 O 3 particles, Ti 3 Al phase, and silicides, especially the precipitated silicides. Eventually, the 0.3 Sc alloy shows optimal thermal stability (the plasticity loss rate 17.3%) and creep resistance (steady-state creep rate 4.4 × 10 –7 s –1 ). The investigation results provide new insights into the mechanism and thermal stability improvement in high-temperature titanium alloys modified by rare earth (RE).
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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) | 0.000 | 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".