Effects of solidification thermal parameters and Bi doping on silicon size, morphology and mechanical properties of Al-15wt.% Si-3.2wt.% Bi and Al-18wt.% Si-3.2wt.% Bi alloys
Bibliographic record
Abstract
The use of bismuth (Bi) in aluminum (Al) industries is recognized as prime importance because of possible recycling as alloying element in special applications such as those enhancing machinability. The effects of Bi interaction on solidification progress as well as on the resulting microstructure of hypereutectic Al-Si alloys, i.e. the morphology and size of Si crystals are rare information. As such, the present research work investigates the characteristic parameters of eutectic and primary Si with addition of Bi for both ternary Al-15 wt.%Si- 3.2 wt.%Bi and Al-18 wt.%Si- 3.2 wt.%Bi alloys samples directionally solidified (DS) under various cooling rates. The alloy containing 15 wt.%Si showed that Bi acts in the suppression of the growth of primary Si crystals for samples solidified at cooling rates ranging from 2.0 to 31 K/s. For lower rates, a minor fraction of five-fold branched Si was recognized in transverse specimens. A mixture of globular-fibrous eutectic Si is shown to occur for eutectic cooling rates higher than 5 K/s. The Al-18 wt.%Si- 3.2 wt.%Bi alloy is characterized by a mixed structure of eutectic flaky Si and primary angular Si with intersecting spines at nearly 90º all over the entire range of experimental cooling rates (i.e., from 0.4 to 40 K/s). Despite the intricacy of the formed microstructures, it was possible to establish relationships between tensile properties and eutectic Si spacing in order to compare the properties between the tested alloys. Tensile strength and elongation decrease with increasing alloy Si content.
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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".