Corrosion and hardness properties of retrogression‐formed and warm‐formed <scp>AA7075</scp> sheet
Bibliographic record
Abstract
Abstract To achieve combined formability and strength in high‐strength precipitation‐hardened aluminum automotive sheets, different forming path used with fast and slow forming technology has been proposed. This study reports hardness and corrosion properties from two elevated‐temperature forming techniques, retrogression forming (RF) and warm forming (WF), employed on AA7075 alloy sheets. Parameters such as optimal pre‐aging, re‐aging, WF, and RF temperature and time were determined. In the retrogression‐formed (R‐F) sheets, initial retrogression treatment of peak‐aged (i.e., T6) temper resulted in significant loss of properties, but final re‐aging step recovered most. When traditional peak‐aging treatment was used to re‐age R‐F sheets, their T6 hardness and corrosion properties were restored to 95%–97% and 86%, respectively, while re‐aging R‐F sheets using industrial post‐forming paint‐bake treatment restored their T6 hardness and corrosion properties to 87%–92% and 78%–79%. In the warm‐formed (W‐F) sheets, increasing pre‐aging temperature and time resulted in increasing properties, and properties of the best W‐F samples matched the R‐F samples. In addition, shorter‐time forming did not significantly reduce properties of formed part, encouraging the use fast‐forming technology. Overall, these findings show that sheets subjected to RF and WF techniques can possess T6 properties in formed parts.
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".