Inclusion Population Evolution in Ti-alloyed Al-killed Steel during Secondary Steelmaking Process
Bibliographic record
Abstract
This paper presents a new approach towards the evolution of non-metallic inclusion (NMI) populations in Ti-alloyed Al-killed steels, based on an extensive inclusion analysis campaign at Tata Steel Europe, IJmuiden Works. Automated SEM techniques were used to characterize the inclusion populations in 120 steel samples taken from nine heats out of two casting series of this steel grade. As NMI in Ti-alloyed Al-killed steels are overwhelmingly dominated by chemically simple Al2O3, most of the process relevant information lies in the analysis of particle size distribution during the secondary steelmaking process. The population density function (PDF) concept was applied, for the first time, to the characterization of inclusion size distributions sampled from secondary steelmaking practice. Two size distribution forms predominate in the entire dataset: i) Lognormal size distributions associated with active nucleation and growth of alumina (deoxidation and reoxidation), indicating net transfer of matter between NMI and solutes in liquid steel and ii) Power-law size distributions, associated with an inclusion population in chemical equilibrium with the melt and subject to collision/breakup processes controlling the distributions. Based on inclusion PDF observations, it is found that the size distribution of alumina inclusions suspended in steel melt, after equilibration and effective float out of large inclusions, tends to approach a Reference Distribution of power-law type function (f(r) = a ⋅ r –3.5) that appears to be a fundamental feature of the alumina-steel system. This Reference Distribution can guide efforts to improve and engineer inclusion populations for a better controlled steel product.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".