Texture-dependent bending behaviors of extruded AZ31 magnesium alloy plates
Bibliographic record
Abstract
The relatively insufficient knowledge of the deformation behavior has limited the wide application of the lightest structure material-Mg alloys. Among others, bending behavior is of great importance because it is unavoidably involved in various forming processes, such as folding, stamping, etc. The hexagonal close-packed structure makes it even a strong texture-dependent behavior and even hard to capture and predict. In this regard, the bending behaviors are investigated in terms of both experiments and simulations in the current work. Bending samples with longitudinal directions inclined from the transverse direction by different angles have been prepared from an extruded AZ31 plate, respectively. The moment-curvature curves and strain distribution have been recorded in the four-point bending tests assisted with an in-situ digital image correlation (DIC) system. A crystal-plasticity-based bending-specific approach named EVPSC-BEND was applied to bridge the mechanical response to the microstructure evolution and underlying deformation mechanisms. The flow stress, texture, twin volume fraction, stress distribution, and strain distribution evolve differently from sample to sample, manifesting strong texture-dependent bending behaviors. The underlying mechanisms associated with this texture dependency, especially the occurrence of both twinning and detwinning during the monotonic bending, are carefully discussed. Besides, the simulation has been conducted to reveal the moment-inclination angle relation of the investigated AZ31 extruded plate in terms of the polar coordinate, which intuitively shows the texture-dependent behaviors. Specifically, the samples with longitudinal directions parallel to the extruded direction bear the biggest initial yielding moment.
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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".