The Role of Interfacial Precipitates on Creep Behaviour of Power Metallurgy (PM) Ti-48Al-2Cr-2Nb+1W Alloy
Bibliographic record
Abstract
Pre-alloyed powders with a nominal composition of Ti-48Al-2Cr-2Nb+1W were consolidated by hot isostatic pressing (HIP). After the HIP process, a step cooled heat treatment (SCHT) with a carefully controlled cooling rate was applied to homogenize the HIP’ed microstructure and produce a fully lamellar microstructure. Following the SCHT, various isothermal aging at 950 °C and step aging processes form interfacial precipitates at the lamellar interfaces. The morphology, size, and distribution of the precipitates are dependent on the aging condition. Creep tests were carried out in air at 760 °C and 276 MPa to investigate the effect of interfacial precipitates. Primary creep resistance and creep life of the 8 and 144 hr aged conditions are improved substantially compared to the unaged condition due to the existence of the interfacial precipitates. However, the step aging process improves the creep resistance only slightly, probably because of the size and distribution differences of the interfacial precipitates compared to the 144 hr aged condition. Microstructure control is important since it has a substantial influence on creep behavior, especially primary creep resistance.
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".