Deformation heterogeneity and its effect on the recrystallisation texture of non-oriented electrical steel
Bibliographic record
Abstract
Non-oriented electrical steel (NOES) is the most commonly used material for the manufacturing of soft magnetic cores for electric motors. The magnetic properties of the steel lamination, which have a significant effect on the energy efficiency of electric motors, are highly dependent on the microstructure and crystallographic texture of the steel. The final microstructure and texture of the NOES are closely related to the thermomechanical processing history of the material, especially the deformed microstructure and texture before final annealing. This is because both nucleation and grain growth during recrystallisation are determined by the microstructure/substructure and texture of the material after cold deformation. During cold rolling, the plastic deformation does not occur in a homogeneous manner in metals, which usually leads to heterogeneous deformation substructures, including shear bands, deformation bands, transition bands, etc., which play a crucial role in determining the final microstructure and texture. In this paper, the formation of deformation heterogeneity during cold rolling of NOES and its effect on the subsequent recrystallisation are investigated using electron backscatter diffraction (EBSD) techniques. The relationships between the deformation substructures and the recrystallisation texture are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".