Microstructure and mechanical properties of a low-alloyed Mg–Zn–Al–Ca alloy: Effect of extrusion speed
Bibliographic record
Abstract
A low-alloyed Mg-1.2Zn-0.6Al-0.1Ca (wt.%) alloy was extruded at 200 °C with different ram speeds (0.5–4.0 mm/s), and the microstructure and mechanical properties were studied systematically. Heterostructures with fine dynamic recrystallized (DRXed) grains and coarse unDRXed grains were achieved at lower ram speeds of 0.5 mm/s and 1.0 mm/s, and fully-DRXed microstructure was attained at 4.0 mm/s. Increasing the extrusion speed resulted in an increase in DRXed grain size from 0.9 μm to 3.8 μm, and a transformation of the DRXed texture component from <10 to 10>−<11–20> to a new orientation that deviated by approximately 14°. The sample extruded at 0.5 mm/s presented an excellent tensile yield strength (TYS) of 369 MPa along with a 7.8% elongation, which was mainly due to the high hetero-deformation induced (HDI) strengthening provided by its heterostructures. Increasing ram speed resulted in an improved elongation despite a decreased TYS. The reasons for the decreased strength with increasing extrusion speed were mainly associated with grain growth, reduced dislocation density and weakened HDI strengthening. The reasons for the improved ductility with increasing extrusion speed were largely due to the increased DRXed grains fraction with soft orientations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".