Improved Slags for ESR Processing of High-Carbon Chromium Bearing Steel
Bibliographic record
Abstract
Freckles severely affect the quality of high-carbon chromium bearing steel ingots produced by electro-slag remelting (ESR). With conventional slags, reducing the melt rate of the electrode can prevent freckle formation, but severe surface defects can still occur. In order to design an appropriate slag for control of segregation and also improve the surface quality of the ingots, the melting temperature, heat transfer properties, and viscosity of several synthetic slags based on the system CaF2–CaO–Al2O3–MgO were evaluated. As a consequence of the laboratory investigation, a slag with 50% CaF2 and a CaO/Al2O3 ratio of 1.5, was selected as a candidate for validation on ESR production facilities based on the following package of attractive properties: low melting temperature, low break temperature, low viscosity and high thermal conductivity. Following evaluation of the selected slag formulation on full-scale plant trials, HSLA steel ingots were produced that were free from freckles and exhibited good surface quality.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 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".