Examination and Simulation of Silicon Macrosegregation in A356 Wheel Casting
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Macrosegregation is commonly seen and has been extensively studied in large ingots in a variety of alloy systems. In comparison, this defect is rarely investigated in small aluminum shape castings. To address this shortcoming, a numerical model was developed to investigate silicon macrosegregation during the low-pressure die casting of aluminum alloy (A356) automotive wheels. The model results were compared with silicon distribution maps measured using an optical, phase area-based technique. The model of the wheel casting process was implemented within FLUENT, a commercial Computational Fluid Dynamics (CFD) software package. In the formulation adopted, liquid metal flow is driven solely by solidification shrinkage due to the variation in density between the liquid and solid phases. Buoyancy and die filling have been ignored. Additionally, the model includes Darcy flow in the two-phase mushy zone, the release of latent heat, and solute redistribution at the micro-scale using the Scheil approximation. The model was validated against temperature and segregation data taken from a commercially cast wheel and shown to be qualitatively correct in predicting trends in temperature histories and segregation. A closer inspection of the data reveals that the model is quantitatively accurate within 10–30%, depending on the location.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it