Warm Forming Simulation of a ZEK100 Magnesium Door Panel
Bibliographic record
Abstract
Abstract The warm forming of a door panel made of commercial grade ZEK100 magnesium alloy sheet was modelled using finite element techniques (Autoform). The operation consisted of a heating step, a forming step and a cooling/springback step. The material properties of the ZEK100 blank were modelled using a set of temperature- and strain rate-dependent stress-strain curves, which were derived based on a Zerilli-Armstrong constitutive model. In order to simplify the material model to enable its complex response to be represented within a commercial finite element code, the anisotropy of the magnesium sheet was approximated using a Banabic-2005 yield surface and yield asymmetry was neglected, a reasonable approach for warm forming. Necking was predicted using a set of forming limit curves obtained at different isothermal temperatures. The entire forming model was run at a punch speed of 160 mm/s, and two initial blank temperatures: 215 and 230 °C. The tooling was initially at room temperature. The model predicted that the blank cracked when its initial temperature was 215 °C due to excessive cooling. The best formability (lack of wrinkling and necking) was predicted when the blank was heated to 230 °C. These predictions agree well with the forming trial outcomes.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".