Prediction of solidification behaviour via microstructure models based on granular structures
Bibliographic record
Abstract
Two important factors affecting hot tearing – semi-solid constitutive behaviour and grain percolation – have been simulated through the use of microstructure models based on granular structures. The semi-solid model geometry is based on a modified Voronoi tessellation, and includes rounded corners to approximate an equiaxed-globular grain structure with liquid surrounding the grains. The percolation model combines solidification and thermodynamic aspects to predict the gradual transition within the mushy zone from a continuous liquid to a coherent solid network, while the constitutive behaviour model uses experimentally-derived data to describe the behaviour of the solid grains. By performing a series of model runs over ranges of grain size and fraction solid, the simulations have revealed an important link between grain size, semi-solid yield stress, strain localisation, and grain coalescence. Furthermore, the models provide insight on the relative importance of each mechanism on hot tear formation, and show promise for improving quantitative hot tearing predictions.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".