Microstructure and Hardness of an Al–8 wt%Si–2.5 wt%Bi Alloy Subjected to Solidification Cooling Rates from 0.1 to 800 K s<sup>−1</sup>
Bibliographic record
Abstract
This work explores the effect of the addition of bismuth (Bi) to Al–8 wt%Si alloys. Bi in Aluminum based alloys works as a self‐lubricating agent, improving machining and wear properties. As Bi is a soft material, it is essential to evaluate how it affects microstructural features and the resulting properties of Al–8 wt%Si alloys. Herein, this hypoeutectic alloy is modified by the addition of 2.5 wt%Bi and subjected to three solidification techniques: differential scanning calorimetry, transient directional solidification, and impulse atomization. Thus, this work investigates the effect of Bi in samples solidified under a wide range of cooling rates. Of specific interest is how Bi modifies the eutectic silicon morphology and alloy hardness compared with a hypoeutectic Al–10 wt%Si alloy from the literature. The silicon (Si) morphology of Al–8 wt%Si–2.5 wt%Bi transitions from flaky (coarse) to fibrous (fine) at a critical cooling rate of 1100 K s−1. Through the combination of Bi addition and processing through impulse atomization, the ternary Al–Si–Bi alloy achieves improvements in hardness of up to 20% compared to Al–10 wt%Si. This is despite having a coarser eutectic microstructure than the binary hypoeutectic Al–Si alloy. This is due to Bi modifying the morphology of the eutectic Si.
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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) | 0.002 | 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".