Continuous-annealing method for producing a flexible, curved, soft magnetic amorphous alloy ribbon
Bibliographic record
Abstract
A method has been developed for continuous annealing of an amorphous alloy ribbon moving forward at several meters per second, giving a curved shape to the ribbon that remains flexible afterward and can be easily wound into a toroidal core with excellent soft magnetic properties. A heat pulse was applied by a compact system on a Metglas 2605HB1 ribbon moving forward at 5 m/s to initiate a thermal treatment at 460 °C, near crystallization onset. The treatment duration was less than 0.1 s, and the heating and cooling rates were above 10 000 °C/s, which helped preserve most of the alloy as-cast ductility state. Such high temperature rates were achieved by forcing a static contact between the moving ribbon and a temperature-controlled roller. A tensile stress and a series of bending configurations were applied on the moving ribbon during the treatment to induce the development of magnetic anisotropy and to obtain the desired natural curvature radius. The core losses at 60 Hz of a toroidal test core wound with the resulting ribbon are lower than the specific values reported by the alloy manufacturer. This method can be implemented at the casting plant for supplying a low-cost, ready-to-use ribbon, easy to handle and cut, for mass production of toroidal cores for distribution transformer kernels (core and coil only), pulse power cores, etc.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".