AN ANISOTROPIC AXISYMMETRIC MODEL FOR WROUGHT MAGNESIUM ALLOYS
Bibliographic record
Abstract
The global environmental concerns have put the light weighting of structures on the fore front of the research in transportation industry. Among other light weight alloys, the transportation industry is considering magnesium intensive light body-in-white structure in automotive applications. Although the research in modeling technique areas is very active, a suitable practical model mimicking the severe asymmetry and anisotropy of magnesium is lacking. Loading-unloading behavior of wrought magnesium alloy over a wide range of strain has been obtained experimentally and is presented here. It is shown that while the material behaves elastically isotropic, it shows a different yield in tension and compression with a high Bauschinger effect. This is attributed to the magnesium multiple deformation mechanisms of slip, extension/contraction twinning, and de-twinning resulting in an asymmetric yield and a direction dependent performance. Up-to-date there are no plasticity model commercially available that can capture these behavior. Therefore, it is necessary to develop a simple and efficient model that can serve as benchmarking tool for plasticity models evaluation. Such model is presented in this paper. The axisymmetric elastic-plastic model of Jahed and Dubey (1997) has been extended to wrought magnesium alloys. An asymmetric yield function is adapted and the obtained behavior in loading and unloading is directly incorporated in the solution process. It is shown that the results are significantly different from isotropic assumptions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".