Textures of Non-Oriented Electrical Steel Sheets Produced by Skew Cold Rolling and Annealing
Bibliographic record
Abstract
In order to investigate the effect of cold rolling deformation mode and initial texture on the final textures of non-oriented electrical steels, a special rolling technique, i.e., skew rolling, was utilized to cold reduce steels. This not only altered initial textures but also changed the rolling deformation mode from plane-strain compression (2D) to a more complicated 3D mode consisting of thickness reduction, strip elongation, strip width spread and bending. This 3D deformation induced significantly different cold-rolling textures from those observed with conventional rolling, especially for steels containing low (0.88 wt%) and medium (1.83 wt%) amounts of silicon at high skew angles (30° and 45°). The difference in cold-rolling texture was attributed to the change of initial texture and the high shear strain resulting from skew rolling. After annealing, significantly different recrystallization textures also formed, which did not show continuous <110>//RD (rolling direction) and <111>//ND (normal direction) fibers as commonly observed in conventionally rolled and annealed steels. At some skew angles (e.g., 15–30°), the desired <001>//ND texture was largely enhanced, while at other angles (e.g., 45°), this fiber was essentially unchanged. The formation mechanisms of the cold rolling and recrystallization textures were qualitatively discussed.
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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 | 0.000 | 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".