Effect of Annealing Temperature on the Selective Oxidation and Reactive Wetting of a 0.1C-6Mn-2Si Advanced High Strength Steel During Continuous Galvanizing Heat Treatments
Bibliographic record
Abstract
The effects of peak annealing temperature and annealing time on the selective oxidation and reactive wetting of a prototype medium-Mn Fe-0.1C-6Mn-2Si third generation advanced high strength steel were investigated. Annealing heat treatments were carried out in a N2-5 vol% H2 243 K (−30°C) dew point process atmosphere at 963 K (690°C) and 1073 K (800°C) for 120 s and 600 s. TEM observations of the sample cross-sections revealed internal oxidation of the subsurface grains and grain boundaries. EELS results showed that the internal oxide network had a multi-layered structure with SiO2 at the oxide core and MnSiO3 as the surrounding shell; however, MnO was the only species detected at the surface of all samples. The effect of annealing temperature on the surface structure development and its impact on reactive wetting of the substrates annealed for 120 s at both peak annealing temperatures by a Zn-0.20 wt% Al (dissolved) galvanizing bath was also determined. In contrast to the 1073 K steel, the 963 K substrate showed superior reactive wetting, owing to the much thinner, finer and wider spacing of the MnO nodules on the pre-immersion surface. TEM+EELS analysis of the coated steels showed that infiltration of the bath alloy and partial reduction of MnO resulted in lift-off of the surface oxides and partial formation of Fe2Al5ZnX interfacial layer, indicating that reactive wetting had occurred for the 963 K × 120 s substrate.
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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) | 0.001 | 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".