Anisotropic yielding and spontaneous shear plastic deformation of extruded AZ31 alloy in σ-τ space: Inverse Swift effect
Bibliographic record
Abstract
The physical mechanism underlying the generation and evolution of the inverse Swift effect in hexagonal close-packed (HCP) Mg alloy has not been clarified. The traditional yield surface is orthogonal to the normal stress direction during free rotational tension (FRT) due to the uni-directional pre-loading paths, which contradicts the inverse Swift effect experimentally observed. Therefore, multi-step loading experiments including free end torsion (FET) and FRT have been performed. Based on specially designed non-proportional loading paths changing in elastic domain, the evolution mechanism of the yield surfaces and anisotropic hardening behaviors in σ-τ space were explored. It is found that the {10_12} tensile twinning introduced by FET leads to the rotation and distortion of the initial yield surface (IYS). Both Swift- and inverse Swift effects can be detected simultaneously by IYS and subsequent yield surface (SYS) by current designed loading paths. The recoverable shear plastic deformation is driven by residual shear stress after the complete nonlinear unloading. Reverse rotation is accelerated by detwinning until the initial twins are exhausted. A new-type inverse Swift effect, i.e., spontaneous positive rotation during FRT, is found to be dominated by local heterogeneous strain and orientation inhomogeneity.
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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".