Dynamic Ferrite Formation and Evolution above the Ae3 Temperature during Plate Rolling Simulation of an API X80 Steel
Bibliographic record
Abstract
Thermo-mechanically controlled rolling is a technique used to produce steel strips and plates. One of the steels widely used in the production of heavy plates for application in oil and gas pipelines is API X80. The hot rolling process of this family of steels consists of applying deformation passes at high temperatures, mainly above Ae3, inside the austenite phase field. It has been shown that during deformation, the phenomenon of dynamic transformation (DT) of austenite into ferrite leads to lower hot deformation resistance within the stable austenite region. In this investigation, hot torsion simulations of an industrial rolling process under continuous cooling conditions were used to monitor the formation of ferrite by DT. Stress–strain flow curves and equivalent mean flow stresses followed by sample characterization via optical and electron microscopy showed the inevitable formation of ferrite above the Ae3. The employed 10-pass deformation schedule was divided into 5 roughing and 5 finishing passes, thereby promoting an increased volume fraction of ferrite and decreased critical strain for the onset of DT and dynamic recrystallization (DRX). A microstructural analysis confirmed the formation of ferrite from the first roughing strain until the last finishing pass. The volume fraction of DT ferrite increased due to strain accumulation, an increased number of deformation passes and as the temperature approached the Ae3, leading to a characteristic torsion texture at the end of the simulation.
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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".