Effect of aluminium content on the formation of inclusions in Fe–5Mn– <i>x</i> Al steels
Bibliographic record
Abstract
The effect of Al content on the characteristics and formation of inclusions in the light-weight Fe–5Mn–xAl steels was investigated in this study. Four synthetic steels with different Al content were produced in the laboratory. The types of observed inclusions were Al2O3 (pure), Al2O3–MnS, AlN(pure), AlN–MnS, AlON–MnS, AlON and MnS. Increasing Al content from 0.5% to 6% increased the total amount of inclusions by 2.5 times. As the Al content increased from 0.5% to 3%, the number of AlN–MnS inclusions increased significantly. Moreover, the AlN(pure) inclusions appeared in 6% Al containing steel. Thermodynamic calculations confirmed that AlN inclusions formed during cooling of the steel. It is also observed that AlN can precipitate on Al2O3 to form AlN + Al2O3 inclusions, classified as multi-phase AlON inclusions in this study. Furthermore, MnS inclusions could co-precipitate with AlN and Al2O3 inclusions, but it preferred to co-precipitate with AlN inclusions.
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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) | 0.000 | 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 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".