Enhancing the Hardness and Compressive Response of Magnesium Using Complex Composition Alloy Reinforcement
Bibliographic record
Abstract
The present study reports the development of new magnesium composites containing complex composition alloy (CCA) particles. Materials were synthesized using a powder metallurgy route incorporating hybrid microwave sintering and hot extrusion. The presence and variation in the amount of ball-milled CCA particles (2.5 wt %, 5 wt %, and 7.5 wt %) in a magnesium matrix and their effect on the microstructure and mechanical properties of Mg-CCA composites were investigated. The use of CCA particle reinforcement effectively led to a significant matrix grain refinement. Uniformly distributed CCA particles were observed in the microstructure of the composites. The refined microstructure coupled with the intrinsically high hardness of CCA particles (406 HV) contributed to the superior mechanical properties of the Mg-CCA composites. A microhardness of 80 HV was achieved in a Mg-7.5HEA (high entropy alloy) composite, which is 1.7 times higher than that of pure Mg. A significant improvement in compressive yield strength (63%) and ultimate compressive strength (79%) in the Mg-7.5CCA composite was achieved when compared to that of pure Mg while maintaining the same ductility level. When compared to ball-milled amorphous particle-reinforced and ceramic-particle-reinforced Mg composites, higher yield and compressive strengths in Mg-CCA composites were achieved at a similar ductility level.
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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".