Influence of hot top geometry on columnar-to-equiaxed transition in a 12 MT steel ingot
Bibliographic record
Abstract
In the present work, the impact of hot top geometry and thermal history on the Columnar-to-Equiaxed Transition (CET) point, of a 12 MT steel ingot was determined using finite element modeling. Experimental validation of the model was conducted on an industrial-size ingot, focusing on temperature, macrosegregation, and shrinkage microporosity. The anticipated Columnar-to-Equiaxed Transition point, influenced by the interaction of solid front rate, thermal gradient, and solid fraction was considered in the analysis. The findings revealed a shift in the CET position in new configurations, up to 56 mm, 63 mm, and 60 mm from the ingot wall in the bottom, middle, and top of the ingot, respectively. The changes are attributed to variations in the kinetics of solidification, particularly the solidification time. Thermo-mechanical phenomena, encompassing mold filling, cooling, solutal convection, and flow driven by shrinkage, were incorporated into the model to predict macrosegregation and the risk of porosity and shrinkage cavity formation for different hot top geometries. A criterion is proposed that allows mitigating macrosegregation and minimizing the risk of porosity and shrinkage cavity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".