Quasi in-situ EBSD characterization of the evolution of microtexture and microstructure in non-oriented electrical steels during cold rolling and annealing
Bibliographic record
Abstract
Abstract Non-oriented electrical steel sheets are extensively used to manufacture core laminations for electric motors. The microstructure and crystallographic texture of the final thin sheets have a significant effect on the magnetic quality of the lamination core. Controlling the development of microstructure and texture during thermomechanical processing of these steels is of great practical importance. Although the final microstructure and texture of the steel sheets are affected by all the processing procedures employed, cold rolling and final annealing have the most direct effect since these are usually the final steps to process the steel. Although much effort has been made to study the evolution of microstructure and texture in non-oriented electrical steels, the formation mechanisms of the final microstructure and texture during annealing (especially grain growth) are still not completely understood, which is partially due to the fact that it is very difficult to directly investigate these processes using conventional characterization techniques. In this research, a quasi in-situ electron backscatter diffraction (EBSD) technique was presented, which enabled the tracking of the changes of the morphology and orientation of individual grains during cold rolling and the subsequent annealing process. The experiments revealed the rotations of individual grains under plane strain compression, and tracked the formation of nuclei from the deformed matrix and the growth of new grains during the recrystallization process. The results provided valuable insights into the evolution of microstructure and microtexture during cold rolling and annealing of non-oriented electrical steels, and helped understand the mechanisms that govern the nucleation and grain growth during recrystallization.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".