Influence of a Laser Irradiation and Laser Scribing on Magnetic Properties of GO Silicon Steels Sheets Using a Nanosecond Fiber Laser
Bibliographic record
Abstract
Improving the performance of electrical steels within the magnetic circuits is essential to save energy. The domain refinement through local surface treatment by laser is an effective technique to reduce the iron losses in grain-oriented iron silicon steels. To interpret the mechanism of this technique, we have quantitatively studied the impact of nanosecond pulse laser treatment on the magnetic properties of grain-oriented Fe(3%wt)Si sheets. We measured the total power loss and apparent permeability of the samples using a Single-Sheet Tester (SST). The laser treatment resulted in a loss reduction of up to 24% compared to the average power loss of standard samples at 50 Hz. At mid-induction levels, the reduction was also accompanied by an improvement in apparent permeability. A dynamic magnetic behavior law was used to identify a dynamic property Λ including information on density, surface area and wall mobility and another internal permeability property µ representative of static field and magnetization characteristics. Lastly, we presented the behavior of these properties under different laser treatment.
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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".