High speed continuous casting of steel billets: Part 2: Mould heat transfer and mould design
Bibliographic record
Abstract
A high speed instrumented mould trial was conducted under industrial conditions to study the heat transfer at the midfaces and corners of the mould and to clarify mould taper requirements in high speed continuous casting. The predicted peak heat transfer in this high speed system was found to be up to 2·5 times that reported for conventional speeds, and up to 1·5 times that for other reported high speed systems. The average heat transfer was found to be up to 45% greater than in conventional systems, and comparable with other high speed systems. The effect of casting speed was analysed in detail and was found to be dependent on carbon content. Increased casting speed was also found to increase the metal level standard deviation but to have less of an impact on the heat transfer than similar changes at conventional speeds. A mathematical billet thermal and solidification model was applied to these heat transfer results to determine the response of the current mould to high speed casting conditions. Using this assessment of the mould distortion and billet dimensions, new mould tapers were designed on the basis of minimising any mould-strand interaction and/or binding. New mould taper designs for high and low carbon grades were recommended for casting speeds of 3·0, 3·5, 4·0, and 4·5 m min -1. The design sensitivity to changes in casting speed is discussed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".