Deep Drawing Parameters and Characteristics for the Hot Forming of AA7075
Bibliographic record
Abstract
The hot forming of 7000 series aluminum alloys, or die quenching (DQ), consists of solutionizing the blank in a furnace and subsequently stamping it in a chilled die set. Being a relatively new technology, adopting the DQ process into automotive assembly lines involved several challenges. One of these challenges is the constitutive modelling, which is being addressed in literature. Another challenge is determining the proper formability conditions for DQ to work. To this end, the work presented herein investigated different forming parameters required to successfully deep draw AA7075 discs. Two disc sizes were selected: 177.8 and 203.3 mm. Parameters that were investigated were: binder load and lubricant applied. The deep draw experiments were also modelled in the LS-Dyna finite element code, using the constitutive data and a Barlat-2000 yield surface developed in other work by the authors. The predicted deep draw curves were found to match well the experimental data. The earring profiles also matched well.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".