Magnetic Barkhausen Noise Characterization of the Recrystallization of a Non-Oriented Electrical Steel after Cold Rolling at Different Angles to the Hot Rolling Direction
Bibliographic record
Abstract
Non-oriented electrical steel sheets are the most commonly used material for the manufacturing of magnetic cores for electric motors and generators. The microstructure and texture of the steel after final annealing have a significant effect on the magnetic properties of the lamination core. To investigate the effect of cold rolling and annealing on the magnetic properties of the steel sheets, a 0.9 wt% Si non-oriented electrical steel was cold rolled at different angles to the hot rolling direction (HRD) and annealed at various temperatures (600°C to 750°C) to produce dissimilar microstructures. The progress of recrystallization was characterized by electron backscatter diffraction (EBSD), and the magnetic response of the steel at various stages of recrystallization was evaluated by magnetic Barkhausen noise (MBN). A number of MBN parameters, e.g. the root mean square, the smoothed envelope, the peak, the full width at half maximum (FWHM) of the envelope, the time integral of the MBN signals and the MBN energy, were analyzed with respect to the fraction of recrystallization during annealing. The results show that cold rolling at different angles to the hot rolling direction induces various deformation microstructures and stored energies, which, in turn, lead to considerably different recrystallization behaviours during annealing. The difference in recrystallization of these materials is also reflected in the MBN parameters.
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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 | 0.001 | 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".