A quasi-in-situ EBSD study on mechanical response and twin variant selection of a hot-rolled AZ31 magnesium alloy
Bibliographic record
Abstract
• Uses quasi-in-situ EBSD to analyze twinning behavior under compression. • Highlight strain hardening and texture evolution driven by extension twinning and Basinski effect. • Reveals twin variant selection's role in texture evolution and mechanical response. • Demonstrate local strain accommodation effects on anomalous twin activation. The selection of twin variants plays a critical role in shaping the deformation texture and mechanical properties of magnesium alloys that are limited by slip systems and diverse twinning modes. In this study, we investigated the twin variant selection and the effect of twinning activity on the strain hardening of a hot-rolled AZ31 magnesium alloy by quasi-in-situ EBSD. Moreover, the Schmid factors and the displacement gradient tensors were computed to evaluate the activation of twin variants. The results reveal that the yield strength increased progressively after each deformation step, driven by grain subdivision and texture hardening induced by extension { 10 1 ¯ 2 } twinning and the Basinski effect at large strains. The nucleation and growth of the { 10 1 ¯ 2 } twins occurred either sequentially or simultaneously during the plastic deformation. At low plastic strains, the activation of most twin variants followed the high Schmid factor criterion while the other twin variants with lower Schmid factors were activated due to the interactions with preexisting twins characterized by high misorientation angles (around 60°). Additionally, this non-Schmid factor scenario was also attributed to low coordinated strain requirements from neighboring grains, showing the critical role of local deformation accommodation in the twinning process. These findings advance the fundamental understanding of the twin variant selection and its implications for the microstructure-property relationship in magnesium alloys for structural applications.
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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".