Influence of different extrusion temperatures on microstructure and mechanical properties of Mg-Bi-Zn-Ca alloy
Bibliographic record
Abstract
The combination of Ca-doped magnesium alloy and hot extrusion process has garnered significant attention due to its exceptional mechanical properties. For this purpose, Mg-Bi-Zn-Ca alloy with a low Ca content is prepared and subjected to hot extrusion at temperatures of 225, 250, and 275 °C respectively. Subsequently, the impact of hot extrusion temperature on Mg alloy's microstructure and mechanical properties is further investigated through high-performance characterization techniques and tensile experiment. The Mg alloy demonstrates exceptional yield strength and ultimate tensile strength of 382.5 MPa and 392.4 MPa at 225 °C, surpassing other Mg alloys of the same type. The strengthening of the alloy primarily arises from the synergistic effect of grain boundary reinforcement and dislocation strengthening, as the Mg alloy exhibits a bimodal grain structure at 225 °C with the smallest average grain size . The interface between DRXed grains and unDRXed regions exhibits a high density of dislocations, accompanied by a significant presence of nanoscale precipitates within the matrix. These nanoscale precipitates act as effective pinning agents, restricting grain boundary expansion and impeding dislocation motion , thereby further augmenting the strengthening mechanism . Therefore, the low cost, high strength, and simple hot extrusion process make the Mg alloy have good industrial application potential.
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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".