Shell Growth, Surface Quality and Mould Taper Design For High-Speed Casting of Stainless Steel Billets
Bibliographic record
Abstract
An industrial plant trial was conducted on an operating billet casting machine at a Canadian minimill to determine mould wall temperature profiles for different stainless steel grades, a range of casting speeds, mould flux types and oscillation frequencies. Samples of the billets cast at the trial were collected and process variables were recorded. An inverse heat-conduction model was utilized to determine mould heat flux from measured mould-wall temperatures. Mathematical models were utilized to investigate mould/billet interaction and mould taper using the heat flux as input. The results from plant measurements, mathematical models and billet sample evaluations were used to correlate mould thermal response with transverse and longitudinal depressions and oscillation-mark depths for austenitic and martensitic stainless steels.On a effectué un essai en usine avec un appareil à couler les billettes, à un mini-moulin canadien. On a déterminé les profils de température des parois du moule en utilisant différentes catégories d'acier inoxydable, à différentes vitesses de coulée, avec divers types de flux de moule et diverses fréquences d'oscillation. On a ramassé des échantillons de billettes coulées lors des essais et l'on a enregistré les variables du procédé. On a utilisé un modèle inverse chaleur-conduction pour déterminer le flux de chaleur du moule à partir des mesures de température des parois du moule. On a utilisé des modèles mathématiques pour étudier l'interaction moule/billette et le rétrécissement du moule, avec le flux de chaleur comme donnée d'entrée. Les résultats des mesures en usine, des modèles mathématiques et de l'évaluation des échantillons de billettes ont servi à corréler la réponse thermale du moule avec les dépressions transverses et longitudinales ainsi qu'avec la profondeur des marques d'oscillation pour des aciers inoxydables austénitiques et martensitiques.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 teacher head, 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".