Morphology of non-metallic inclusions formed during refining of steel with lanthanum under vacuum induction melting conditions
Bibliographic record
Abstract
Refining of steels and alloys with rare earth metals (REMs) under vacuum induction melting conditions has become widespread due to the high affinity of these metals for oxygen and sulfur. However, this same high oxygen affinity of REMs also leads to reactions not only with dissolved impurities but also with oxide phases that are in contact with the molten metal, such as the crucible material, slag on the surface of the melt and slag deposits on the walls of the crucible. As a result, a larger amount of REMs must be added than is theoretically required to bind oxygen and sulfur in the melt. Moreover, this introduces uncertainty into the calculation of the optimal mass of REM additions. This uncer-tainty is further complicated by several other factors, including the initial concentrations of oxygen and sulfur, melt temperature, and the degree of air leakage into the vacuum chamber. Despite the implementation of process control measures, in practice it is not possible to completely eliminate some variation in the residual concentrations of REMs, oxygen, and sulfur in ingots from different heats. However, even such limited variation results in a significant scatter in the characteristics of REM-containing non-metallic inclusions. At the same time, a direct correlation between the mor-phology of non-metallic inclusions and the total residual REM content is not always observed. Meanwhile, the charac-teristics of inclusions can have a substantial influence on the microstructure and, consequently, on the properties of the metal. This study establishes the correlation between the content of lanthanum not bound to oxygen and sulfur in ingots of structural steel of the 300M type, produced in laboratory vacuum induction furnaces, and the characteristics of non-metallic inclusions, such as chemical composition, size, and number per unit area of polished section
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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 | 0.000 | 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) | 0.000 | 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".