Physical Simulation Based on Dynamic Transformation Under Hot Plate Rolling of a Nb-Microalloyed Steel
Bibliographic record
Abstract
In this work, the presence of dynamically formed ferrite above the Ae3 temperature during the physical simulation of hot rolling was presented. This unusual metallurgical process is known as dynamic transformation (DT). The metastable ferrite phase undergoes a reverse transformation when the temperature is held above the Ae3 by means of a diffusion process. These phenomena affect the rolling load during high-temperature plate rolling. Therefore, a linepipe X70 steel was studied under plate rolling with two-pass roughing and seven-pass finishing strains of 0.4 and 0.2, respectively, applied at strain rate of 1 s−1 and interpasses of 10, 20, and 30 s. The samples were cooling down during deformation, which mimics the actual industrial hot rolling. It was observed that the alloy softens as the hot rolling progresses, as depicted by flow curves and mean flow stress plots, which are linked to the combined effects of dynamic transformation and recrystallization. The former initially occurs at lower strains, followed by the latter at higher strains. The critical strain to DT was affected by the number of passes and temperature of deformation. Shorter interpass time allows higher amounts of ferrite to form due to higher retained work hardening. Similarly, the closer the deformation temperature to the Ae3 permits a higher DT ferrite fraction. The information from this work can be used to predict the formation of phases immediately after hot rolling and optimize models applied to the accelerated cooling.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".