Two‐Step Preaging of an Al–Mg–Si Alloy
Bibliographic record
Abstract
Preaging (PA) is an industrial routine that suppresses the deleterious effect of natural aging and enhances the final paint‐bake hardening of 6XXX aluminum alloys. In view of the different advantages of PA at high and low temperatures, the effects of performing PA in two steps at different temperatures, namely, 80 and 160 °C, are explored. Various two‐step PA combinations are investigated, involving both the temperature orders and various aging times at both temperatures, while aiming at the same hardness after all PA treatments. These results are interpreted on the basis of cluster formation and vacancy evolution during two‐step PA. In particular, the first PA step is found to play a more important role than suggested by the durations of the two PA steps as the clusters formed in the first step can strongly influence the subsequent evolution of both clusters and vacancies in the second step. It is found that two‐step PA results in a compromise between the effects of one‐step PA at both temperatures, that is, the enhancement of natural secondary aging stability is accompanied by a reduced paint‐bake hardening or vice versa.
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) | 0.002 | 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".