Model and validation of NbC nanoprecipitation during TMCP of X70 microalloyed steels
Bibliographic record
Abstract
The effects of niobium (Nb) composition and temperature during the transition from rough rolling (RR) to finish rolling (FR) and during coiling were studied for NbC nanoprecipitation (<10 nm radius) in X70 microalloyed steels . Three (3) X70 microalloyed steels , with a composition of either 0.06 wt% Nb or 0.03 wt% Nb and/or with different cooling rates from rough rolling to finish rolling and/or different cooling interrupt temperatures (high (>550 °C) and low (<550 °C)), were studied. Matrix dissolution, in combination with transmission electron microscopy and X-ray diffraction, were used to quantify the size distribution and volume fraction of NbC nanoprecipitates in each steel. The measured peak radius and volume fraction of NbC nanoprecipitates for the 0.06 wt% Nb steels were 3.0–3.5 nm and 4.1 × 10 −4 , respectively, and 2.0 nm and 2.2 × 10 −4 respectively, for the 0.03 wt% Nb steel. NbC precipitation was modeled using the PRISMA module of Thermo-Calc with the predicted size distributions showing good agreement with the measured NbC size distributions. For the processing conditions studied, model results showed that the majority of nano NbC precipitation occurred during finish rolling. The predicted size distribution of these precipitates was affected by the nominal Nb composition and the finish rolling temperature(s). Model results indicated that nucleation and growth of NbC in the ferrite (for the conditions studied) were negligible. A simulation with increased cooling rate between the end of rough rolling and the start of finish rolling showed a decrease in the size of NbC nanoprecipitates from 3.3 nm to 2.6 nm.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".