Influence of Al and N Content and Cooling Rate on the Characteristics of Complex MnS Inclusions in AHSS
Bibliographic record
Abstract
This study focused on the characteristics of complex MnS inclusions in advanced high strength steels. The effect of metal chemistry (Al and N) and the cooling rate of steel were evaluated by analyzing the inclusions present in five laboratory produced steels. The observed complex MnS inclusions contained Al2O3-MnS, AlN-MnS, and AlON-MnS. An increase in Al content from 0.5% to 6% increased the number of complex MnS inclusions by ~4 times. In comparison, a decrease of ~80% was observed due to the increased N content of steel from <10 ppm to ~50 ppm. MnS precipitation ratio was used to determine the potency of different inclusions forming complex MnS inclusions due to heterogeneous nucleation. It was found that the MnS precipitation ratio of the observed inclusions was related to their misfit with MnS, and it decreased in the order of AlN > AlON > Al2O3. Moreover, it was determined that AlN particles could be easily engulfed at the solidification front relative to Al2O3, which resulted in a higher MnS precipitation ratio for Al2O3 under slow cooling conditions.
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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 | 0.000 |
| 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 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".