Investigation of Inclusion Formation in Light‐Weight Fe–Mn–Al Steels using Automated Scanning Electron Microscope Equipped with Energy‐Dispersive X‐Ray Spectroscopy
Bibliographic record
Abstract
Herein, the effect of Mn content on the characteristics and the formation of inclusions in light‐weight Fe–Mn–Al steels is investigated. Three laboratory‐produced steels, containing different manganese contents (2%, 5%, and 20%) are investigated. 2D and 3D inclusion characterization methods are used to establish inclusion classification rules for oxide, sulfide, and nitride inclusions using an automated scanning electron microscope (SEM) equipped with energy‐dispersive X‐ray spectroscopy (EDS) (ASPEX system). The observed inclusions are classified into Al2O3(pure), Al2O3–MnS, AlN(pure), AlN–MnS, AlON–MnS, AlON, and MnS. The results show that an increased Mn content of steel increases the number of inclusions, especially Al2O3–MnS and AlN–MnS inclusions. In the case of Al2O3–MnS inclusions, Al2O3 inclusions serve as the site for precipitation of MnS. Thermodynamic calculations suggest that the AlN‐containing inclusions formed during cooling and solidification of steels. Moreover, the formation of AlN–MnS inclusions can take place by the nucleation of MnS on AlN inclusions and vice versa.
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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".