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 Al 2 O 3(pure) , Al 2 O 3 –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 Al 2 O 3 –MnS and AlN–MnS inclusions. In the case of Al 2 O 3 –MnS inclusions, Al 2 O 3 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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".