Experimental and Numerical Study of Electromagnetic Forming of AZ31B Magnesium Alloy Sheet
Bibliographic record
Abstract
Wrought magnesium alloys are interesting materials for automotive and aeronautical industries due to their low density in comparison to steel and aluminium alloys, making them ideal candidates when designing a lower weight vehicle. However, due to their hexagonal closepacked (hep) crystal structure, magnesium alloys exhibit low formability at room temperature. For that reason, in this study a high velocity forming process, electromagnetic forming (EMF), was used to study the formability of AZ31B magnesium alloy sheet at high strain rates. In the first stage of this work, specimens of AZ31B magnesium alloy sheet have been characterised by uniaxial tensile tests at quasi-static and dynamic strain rates at room temperature. The influence of the strain rate is outlined and the parameters of Johnson-Cook constitutive material model were fit to experimental results. In the second stage, sheets of AZ31B magnesium alloy have been biaxially deformed by electromagnetic forming process using different coil and die configurations. Deformation values measured from electromagnetically formed parts are compared to the ones achieved by conventional forming technologies. Finally, numerical study using an alternative method for computing the electromagnetic fields in the EMF process simulation, a combination of Finite Element Method (FEM) for conductor parts and Boundary Element Method (BEM) for insulators, is shown.
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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) | 0.001 | 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".