The processing, microstructure, texture, and magnetic properties of electrical steels: A review
Bibliographic record
Abstract
Electrical steels, also known as silicon steels, play an essential role in the generation, transmission, and use of electricity. The magnetic quality of electrical steels and thus the energy efficiency of electromagnetic devices are highly dependent on the thermomechanical processing procedures employed to manufacture the electrical steel sheets. Every processing step, from casting, hot rolling, cold rolling to annealing, introduces a specific microstructure and texture, which influences the microstructure and texture of next processing steps as well as the final magnetic properties. In this paper, both types of electrical steel, i.e., grain-oriented electrical steel (GOES) and non-oriented electrical steel (NOES), are reviewed bearing in mind that NOES has perhaps received less attention till now. The magnetism of ferromagnetic materials and the metallurgical factors that affect the magnetic properties of electrical steels are first briefly discussed. The effect of each thermomechanical processing step on the formation of the microstructure and texture of the final electrical steel sheets is then scrutinised. The status and challenges in optimising the crystallographic texture of electrical steels are discussed. Future directions to the development of energy-efficient and cost-effective electrical steels are pointed out.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".