Effect of NbB2 Addition on the Microstructure and Mechanical Properties of Mechanically Alloyed Al-12.6Si Alloys
Bibliographic record
Abstract
This study reports the effect of NbB2 particles on the Al-12.6 wt.% Si matrix powders and composites in regard of physical, microstructural and mechanical properties.In order to obtain hybrid powders, Al-12.6 wt.% Si-2 wt.% NbB2 blends were mechanically alloyed (MA'd) for various durations (1, 4 and 8 h ) in a Spex™ Mixer/Mill using hardened steel vial/balls with a 7/1 ball-to-powder weight ratio.A hydraulic press with a uniaxial pressure of 450 MPa was used for the compaction of the as-blended/MA'd powders.Green compacts were sintered at 570°C for 2 h under Ar gas atmosphere.X-ray diffractometry (XRD) and scanning electron microscopy/energy dispersive spectroscopy (SEM/EDS) techniques were utilized for the microstructural characterization of the as-blended/MA'd powders and the sintered composites.Differential scanning calorimetry (DSC) analyses were also conducted on the powder products.Sintered samples were characterized by density and Vickers microhardness measurements and sliding wear tests.With increasing MA time, mechanical improvement in composite properties was observed.Reinforcing particles had positive effect on the mechanical properties of the matrix: Al-12.6 wt.% Si-2 wt.% NbB2 MA'd for 4 h showed higher microhardness value (158.50±10.02MPa) and lower wear volume loss (0.161 mm 3 ) than those of Al-12.6 wt.% Si matrix MA'd for 4 h.Al-12.6 wt.% Si matrix MA'd for 4 h showed microhardness value (146.70±8.81MPa) and wear volume loss (0.194 mm 3 ).
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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".