Effect of Rolling Temperature, Reduction and Alloying Additions on the Texture of Warm Rolled Steels
Bibliographic record
Abstract
The effect of warm and cold rolling parameters on the development of annealing textures was studied in two low carbon steels containing additions of chromium. Two warm rolling temperatures (640 and 700°C) were employed together with a reduction of 65%. The effects of an additional cold rolling reduction of 40% and of decreasing the heating rate during annealing were also studied. The ND fiber, <111>//ND, of the recrystallization texture was strengthened as the warm rolling temperature was decreased. However, all the warm rolled steels contained a retained RD fiber, <110>//RD. A noticeable improvement in both the continuity and intensity of the ND fiber was obtained when the sample was submitted to an additional 40% cold rolling reduction. The ND fiber was even more continuous and intense when a low heating rate was utilized, yielding r-values of 1.1 and 1.3 for the warm rolled and warm plus cold rolled samples, respectively.
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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 | 0.001 | 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".