Refreshing Industrially Processed 6xxx Series Aluminum Alloys after Prolonged Natural Aging
Bibliographic record
Abstract
Natural aging (NA) undesirably hardens 6xxx series aluminum alloys and hampers subsequent paint‐bake (PB) hardening, thus limiting the use of material after prolonged storage. It is presented that a refreshment treatment for seconds at a temperature between 230 and 290 °C can effectively lower the hardness/strength of two commercial alloys that have experienced NA for ≈3.5 years and enhance their PB hardening, thus enabling the reuse of the material with minimal energy input. The treatment is based on dissolving solute clusters formed during prior aging, but precipitation during refreshment can compromise its efficacy. The dependence of cluster dissolution and precipitation on the refreshment parameters as well as on the alloy composition is analyzed. A higher temperature is suggested for refreshing AA6014 alloy than for AA6016 alloy due to a higher thermal stability of the clusters in the former. Natural secondary aging (NSA) is investigated and it is proposed that the remaining undissolved clusters play an important role in controlling the mobile vacancy concentration. Refreshment experiments utilizing various heating media demonstrate that the treatment is hardly sensitive to the heating rate which facilitates its industrial implementation. The refreshed alloy can undergo further preaging to enhance the NSA stability and PB hardening.
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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) | 0.001 | 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".