Reduction of Hot Tearing of Cast Semi-Solid 206 Alloys
Bibliographic record
Abstract
The mechanical properties of 206 alloys are among the highest of aluminum alloys. However, these alloys are usually prone to hot tearing. It is known that the addition of silicon can reduce the hot tearing propensity and improve fluidity. However, the commercial 206 alloys used in conventional casting processes limit the silicon concentration ≤0.05 wt% to obtain good mechanical properties. However, the semi-solid forming offers a unique opportunity to increase the silicon content to improve the castability without compromise on mechanical properties. In the present paper, the development of modified 206 alloy compositions to minimize hot tearing during semi-solid forming while maintaining competitive mechanical properties is reported. The effect of high silicon contents with varying copper levels on hot tearing sensitivity is studied. The mechanical properties of a high Si 206 alloy with lowest hot tearing sensitivity are evaluated. It is found that increasing the silicon content in 206 alloys is beneficial to reduce hot tearing. The high Si 206 variants produced by the SEED rheocating process not only reduce significantly the hot tearing sensitivity but also attain superior mechanical properties.
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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".