Solidification model calibration for predicting microstructure fields in HPVDC Aural™-2
Bibliographic record
Abstract
In this paper, the conventional solidification model available in the ProCAST® software is tested and calibrated to predict the microstructure of an aluminium alloy stepped plate produced by the high-pressure vacuum die casting process. An experimental tooling was built to cast Aural™-2 specimens at 1.8, 3.0 and 4.7 mm thicknesses, while measuring the subsurface die temperature gradient at two locations. The measured die temperatures were then used to determine the timedependent interfacial heat transfer coefficients required as input to calculate the cooling rate. The microstructures of 1.8 and 4.7 mm sections were characterized through-thickness using electron backscatter diffraction imaging and energy-dispersive spectroscopy for both the α-Al and eutectic regions. A finite element model was set up in ProCAST® using small subdomains to efficiently calculate the solidification behavior in each region and extract the predicted microstructural characteristic lengths. The model was integrated into an iterative external optimization loop operating on Python to calibrate the six material parameters of the solidification model for this alloy by minimizing the error between the predicted and measured dendritic (α-Al) and eutectic grain sizes. With these optimized parameters, the prediction accuracy was tested for the skin-tocore profile in two section thicknesses typical of structural die casting. Such a model could be used as part of a wider numerical toolbox to predict the location-specific service strength and ductility for structural die castings at the design stage, before the tooling is built.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".