Warm Forming Response of ZEK100 Sheet obtained under Biaxial Stretching with Full-Field Displacement Measurements
Bibliographic record
Abstract
The warm forming response of ZEK100 sheet was studied between 150 °C and 250 °C using hydrostatic bulging coupled with full-field displacement mapping. Due to the strain-rate sensitivity of magnesium alloys at elevated temperature, it was important to ensure that the strain-rate remained reasonably constant during the bulge test. Various gas pressure versus time profiles were used to achieve strain-rates in the range of 0.01 s −1 to 0.1 s −1 . The results from equi-biaxial bulge testing will be detailed in addition to bulge testing performed using elliptical dies with various aspect ratios to generate data under different biaxial stretching conditions. Supplementary characterization was provided using shear specimen data and tension test data from the rolling and transverse directions of the sheet. The biaxial, tensile, and shear data was used to calibrate a linear transformation-based anisotropic yield function at 200 °C. The yield function coefficients were optimized by matching the surface displacement data between experiments and finite element simulations of the bulge test. Agreement of the surface displacement data for a given applied pressure ensured that both the stress and strain in the bulged dome were accurately captured. Finite element simulations of the bulge tests were compared to experimental data to confirm accuracy of the calculated yield function.
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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) | 0.002 | 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".