Die Quench Process Sensitivity of AA7050
Bibliographic record
Abstract
Abstract The current work investigates the sensitivity of AA7050 aluminum alloy sheet to the thermal cycle involved in the die quenching (DQ) process. This entailed the variation of three key parameters in the thermal cycle, namely solutionizing time, transfer time and quench rate, considering both water quenching (WQ) and die quenching (DQ) at various die pressures. Following a lab-grade T6 heat treatment, Vickers hardness (HV-1000) measurements of all test conditions and tensile testing on a reduced number of test conditions were completed. The results showed only limited sensitivity to solutionizing time for the range of conditions tested. Longer transfer times and slower cooling rates both negatively affected final hardness properties, with up to 4% reduction in final hardness relative to the water-quenched T6 condition. Higher cooling rates during die-quenching produced statistically similar final tensile properties to those achieved from water quenching, following a T6 heat-treatment; while slower cooling rates resulted in significant reductions in tensile strength and uniform elongation. Limiting dome height testing of a 101.6 mm dome sample under die-quench conditions produced a major true limit strain of 0.42. The die-quench limit strain compared favourably against the limit strains of the same geometry in the T6 condition at room temperature and 150 °C (isothermal), which were 0.12 and 0.18, respectively.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".