Effect of Iron Content on 319 Semi-Solid Alloys using the SEED Method
Bibliographic record
Abstract
Aluminum-silicon alloys represent a majority of cast alloys used in the automotive industry, as these alloys can be molded into complex shapes with relative ease. Cast aluminum-silicon-copper alloys, or 319 aluminum alloys, for example, are a popular choice in the fabrication of engine blocks. The addition of copper to Al-Si aids in maintaining the casting integrity when in service (with temperatures exceeding 200°C (392F)). Although numerous publications have explored the use of cast 319 alloys, there is still very little in terms of literature with respect to semi-solid cast 319 alloys. Semi-solid casting, where an alloy is prepared as a slurry, is fast becoming a popular casting method due to the dual liquid-solid nature of the resulting billet. The billet remains still, as if solid, yet flows under the action of an applied shear force. A semi-solid rheocast 319 aluminum alloy was prepared for this study using the SEED (Swirled Enthalpy Equilibration Device) casting method, as developed by Rio Tinto Alcan in collaboration with the Aluminium Technology Centre of NRC Canada. This process uses heat extraction of the liquid aluminum alloy via agitation (swirling) in a confined cylinder to form the semi-solid billet. The resulting microstructure consists of alpha aluminum globules surrounded by the eutectic phase. In this work, the iron content of the 319 semi-solid alloy was altered, and the changes in microstructure and mechanical properties were observed. The chief objective of this ongoing study is to use recycled aluminum (with iron traces) for future casting, as opposed to higher purity aluminum. The SEED process itself has proven successful in obtaining high integrity 356/357 castings, among others; therefore, it is also the authors' intent to develop SEED as a fully industrial process.
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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".