Conventional and near net shape casting options for steel sheet <sup>†</sup>
Bibliographic record
Abstract
The conventional continuous casting (CCC) of steel, starting in the 1950s, has now become the dominant casting process, worldwide. It presently accounts for about 1.5 billion tonnes of steel cast per year. Nonetheless, given the inexorable forces favouring near net shape casting (NNSC) of semi-finished products, for reasons of economy of scale, process rationalisation and the protection of the environment, the question of how long CCC can continue to dominate in the casting of steel, must remain a question. Two NNSC candidates for steel are the twin roll casting (TRC) and horizontal single belt casting (HSBC) processes. While the TRC of steel sheet by ‘CASTRIP’ has now been operating commercially for some 12 years within NUCOR, producing low carbon steel sheets, HSBC has only just been commercialised, at Salzgitter’s Peine Plant, in Germany, under the name ‘Belt Cast Technology’. It had to await the strong demand for high strength high ductility steels for the auto industry. The McGill Metals Processing Centre has been involved in both casting processes since 1987, its research being aimed at addressing the various technical aspects and fundamental problems associated with these two NNSC processes for forming steel sheet material. This paper addresses the pros and cons of NNSC processes versus CCC, and the differences between TRC and HSBC, for future steel sheet production.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.021 | 0.007 |
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".