Improvement of Utilization Ratio of Nanoparticles in Steel and Its Influence on Acicular Ferrite Formation
Bibliographic record
Abstract
In the present study, 35MnVS experimental steels containing nanoparticles are manufactured using a vacuum induction furnace (VIF), as well as a vacuum induction levitation furnace (VILF). The differences on the utilization ratio of nanoparticles (URN), inclusion characteristics, and steel microstructure between the original steel and experimental steels are compared. The results reveal that the steel processed with a VILF has a higher URN that helps to form a finer inclusion size range. There is a critical size for each inclusion, and only when the inclusion size is ≤ the critical value, the inclusions can efficiently induce acicular ferrites (AF). Among the three steels, only the inclusion size range of the VILF steel is less than the critical size which is between ≈2.2 and ≈5.2 µ. Therefore, the inclusions in VILF steel have a relatively stronger ability on inducing AF, and that is revealed by its microstructure showing large proportions of AF.
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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".