A Comparative Study of Grain Refining of Al-(7–17%) Si Cast Alloys Using Al-10% Ti and Al-4% B Master Alloys
Bibliographic record
Abstract
The present article addresses solidification parameters, and includes analyses of the macrostructure and microstructure in the light of the results obtained from the thermal analysis, from which it is possible to conclude that undercooling (TS) and recalescence (TR) temperatures increase with the initial increase in titanium (Ti) concentration. If the concentration reaches approximately 0.25%, a rapid decrease in these temperatures is observed. Thereafter, the temperatures increase again with the further increase in Ti concentration, and eventually become constant. These temperatures also vary depending on the superheating and casting temperature. The ∆T parameter (i.e., TS − TR) decreases with the Ti concentration and, from a concentration of around 0.20% Ti, this parameter becomes zero. The grain size decreases with the Ti concentration. If the concentration exceeds about 0.20%, the grain size becomes the minimum. Another parameter to be considered is the interaction between the grain refiner and the traces of other metals in the base Al alloy. For example, Al-4%B can react with traces of Ti that may exist in the base alloy, leading to the reaction between boron (B) and Ti to form TiB2. Grain refinement is achieved primarily with TiB2 rather than AlB2, or both, depending on the Ti content in the given alloy.
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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 | 0.001 |
| 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".