Constitutive Equations for Mg Alloy Hot Work Modeling
Bibliographic record
Abstract
Constitutive equations for hot working are of great importance in optimizing forming processes to balance reductions in preheating and force, to avoid defects and to improve properties. Flow curve shapes and. constitutive parameters are affected by variations in composition, in homogenization, in grain morphology and significantly in texture. Confidence in published analyses is enhanced by existence of many data for the same or similar alloys. In this paper, constitutive equations have been collected for commercial Mg alloys from torsion, compression and tension tests in the range from 10-3 to 10 s-1 and 180 – 500°C. Some data were determined by the authors’ but more came from published reports; in some cases they have been re-calculated in a common manner. The deformation and restoration mechanisms that control the flow curve shape and the material parameters are summarized. Microstructure investigations of strained samples are illustrated. Applications to extrusion or rolling are discussed; comparisons to Al are made as appropriate.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
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".