Recent High Temperature Adventures in the Casting of Metals and their Potential Implications for the Near Net Shape Production of Steel Sheets
Bibliographic record
Abstract
The contacting of a liquid metal alloy with a freezing substrate during twin roll and belt casting operations commonly involves three phases: the contacting liquid metal, the colder substrate and/or superstrate being contacted and an intervening gas phase caught up and squeezed in between the two. Strip products can be cast in a satisfactory manner, or not, depending on interfacial chemical reactions, interfacial gas flows, meniscus behaviour, substrate topology and metal wetting/spreading characteristics. This paper describes a number of interesting experiments and mathematical models that have been devised to understand the nature and role of interfaces and how they bear on casting machine productivity, sheet surface quality and as-cast microstructures. It is shown that the thin layer of gas trapped between the substrate and the melt is an extremely effective source of thermal resistance, highly sensitive to belt surface topography and interfacial 'thickness'. Similarly, the way a liquid metal wets the substrate, either wetting or non-wetting or something in between, is very relevant to a sheet metal surface topography. For steel melts, this interfacial surface of contact is, in the main, non-wetting.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".