Effect of Heating Rate on the Development of Annealing Texture in Nonoriented Electrical Steels
Bibliographic record
Abstract
The magnetic properties of nonoriented electrical steels are influenced by grain size and texture. During the final annealing process, heating rate, annealing temperature and time, and cooling rate are the main factors which can influence the formation of annealing texture. Among these parameters, heating rate is more effective in controlling texture development through changes in the recovery and recrystallization processes. Therefore, it can provide the means to decrease core loss and increase permeability. In nonoriented electrical steels with different initial grain sizes, the effect of heating rate on texture development during final annealing is examined, and the reasons for texture changes are discussed. The average grain size decreases with the increase in heating rate both in the coarse-grained and in the fine-grained specimens. In the coarse-grained specimen, the Goss texture is significantly strengthened but the {111}‹112› texture component is slightly weakened as heating rate increases. On the other hand, in the fine-grained specimen, the {111}‹112› intensity is greatly decreased but the Goss intensity is slightly increased as the heating rate increases. The heating rate up to the annealing temperature affects texture formation differently depending on the initial grain size prior to cold rolling. These differences are mainly related to the difference in the number of shear bands formed during cold rolling in grains having different sizes.
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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 | 0.001 |
| 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".