The Origins of the Goss Orientation in Non‐Oriented Electrical Steel and the Evolution of the Goss Texture during Thermomechanical Processing
Bibliographic record
Abstract
During the manufacturing of non‐oriented electrical steel laminations for electric motor or generator applications, the Goss ({110}<001>) texture frequently appear s in several stages of the processing. To understand the origin and the evolution of this texture, a non‐oriented electrical steel (2.8 wt% Si) i s processed through hot rolling, hot band annealing, cold rolling and final annealing, and the origins of the Goss orientation and the evolution of the Goss texture in these processes a re investigated by electron backscatter diffraction (EBSD) techniques. It i s seen that hot rolling result s in a high volume fraction ( ≈ 30%) of the Goss grains near the surfaces of the rolled plate, but the subsequent hot band annealing significantly reduce s it to less than 10%. After cold rolling, the Goss volume fraction further decrease s to only about 1%. Three locations of the Goss grains a re observed in the cold‐rolled matrix, that is, those within the shear bands of two symmetrically equivalent {111}<112> grains, those embedded in the microbands of one of the {111}<112> grains, and those at the grain boundaries between the {111}<112> and {113}<361> grains. The formation mechanisms of these three types of Goss grains a re explained using a transition band model and a rigid inclusion model.
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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".