Recrystallization Behavior of Non-oriented Electrical Steel Sheets after Skew Cold Rolling
Bibliographic record
Abstract
Stress-relief annealing is an indispensable processing step for non-oriented electrical steel (NOES) sheets to achieve optimal magnetic properties. The annealing microstructure and texture are not only dependent on the annealing conditions, but also on the prior thermomechanical processing history. To investigate the effect of deformation mode on the recrystallization behavior, a NOES containing 0.9 wt% Si was cold rolled by skew rolling in which the hot-rolled-and-annealed plate was fed at 45° into the rolls to change both the initial texture and the deformation mode. The skew-cold-rolled sheets and those rolled by conventional and cross rolling were then annealed at different temperatures (650 to 1050 °C) for different times (0.5 to 30 min). The recrystallization behavior was characterized using electron backscatter diffraction (EBSD) techniques. It was found that the cold rolling deformation mode and the initial texture have a significant effect on the recrystallization behavior. The recrystallization rates of the skew- and cross- rolled sheets are higher than that of conventionally rolled steel if the annealing temperature is low (650 °C) or the annealing time is short (0.5 min). When the annealing temperature is relatively high (850 °C) and the annealing time is relatively long (2 min), the difference in recrystallization rate is small. In all the cases, skew rolling promotes the formation of the desired <001>//ND (normal direction) texture, and the unfavorable <111>//ND texture is essentially eliminated.
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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".