Effect of Number of Roughing Passes on the Dynamic Transformation of Austenite during Simulated Plate Rolling
Bibliographic record
Abstract
When austenite is deformed within the austenite phase field, it partially transforms dynamically into ferrite. Here, plate rolling simulations were carried out on an X70 steel using rough rolling passes of 0.4 strain each. The influence of the number of roughing passes on the grain size and volume fraction of induced ferrite was determined. Up to three roughing passes applied at 1100 °C followed by 5 finishing passes at 900 °C were employed. The sample microstructures were analysed by means of metallographic techniques. Both the critical strain to the onset of dynamic transformation as well as the grain size decreased with pass number during the roughing simulations. For the finishing passes, the mean flow stresses (MFS`s) applicable to each schedule decreased when a higher number of roughing passes was applied. The volume fraction of dynamically formed ferrite retained after simulated rolling increased with the roughing pass number. This is ascribed to the increased amount of ferrite retransformed into austenite and the finer grain sizes produced during roughing. The forward transformation is considered to occur displacively while the retransformation into austenite during holding takes place by a diffusional mechanism. This indicates that both dynamic transformation (DT) and dynamic recrystallization were taking place during straining.
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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 | 0.000 | 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".