Bibliographic record
Abstract
Thermal stability, determining the material ability of retaining its properties at required temperatures over extended service time, is becoming the next frontier for aluminum alloys. Its improvement would substantially expand their range of structural applications, especially in automotive and aerospace industries. This report explains the fundamentals of thermal stability; definitions, the properties involved; and the deterioration indicators during thermal/thermomechanical exposures, including an impact of accidental fire, and testing techniques. For individual classes of alloys, efforts aimed at identifying factors stabilizing their microstructure at service temperatures are described. Particular attention is paid to attempts of increasing the current upper service limit of high-temperature grades. In addition to alloying aluminum with a variety of elements to create the thermally stable microstructure, in particular, transition and rare-earth metals, parallel efforts are explored through applying novel routes of alloy processing, such as rapid solidification, powder metallurgy and additive manufacturing, engineering alloys in a liquid state prior to casting, and post-casting treatments. The goal is to overcome the present barriers and to develop novel aluminum alloys with superior properties that are stable across the temperature and time space, required by modern designs.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".