Ensemble Prediction of Tundish Open Eyes Using Artificial Neural Networks
Bibliographic record
Abstract
As global steelmakers are feeling the economical pinch, the need for improving quality and quantity using what is already readily available, increases. This gap in achievement can be bridged by innovation and perforation of already existing techniques and methodologies from other fields. Steel quality, an important issue, is often not associated with a phenomenon known as tundish open eyes. However, recently researchers have shown the detrimental effects of reoxidation and the deterioration of the final product (slabs/billets). Understanding the formation of this event, and mitigating the formation will be an important issue to solve. Current models investigating the former have existed largely in the computational fluid dynamics modelling domain. However, the solution for the former, can only provide static recommendations thus are less useful in a dynamic environment. Hence, development of a reliable model which has the ability to “learn on the fly” is very much needed. In the current study, artificial neural network models have been used to predict non-dimensional open eye sizes in the tundish. The dataset has been compiled from previous regression formulations. The performance of the models is determined based on the following metrics 1) coefficient of multiple determination (R2), 2) and root mean square error (RMSE). The ANN based models, show significant promise, in particular the ensemble variants, which have shown increased accuracy and stability across all domain and range.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".