Cold Spray Additive Manufacturing of SmCo-Al Permanent Magnets
Bibliographic record
Abstract
Abstract A key factor to ensure a sustainable future for the air transport industry is electrification. Exploring new designs for permanent magnet electric motors could increase their potential. Unfortunately, complex shaped magnets cannot be produced easily using current fabrication methods. Cold spray additive manufacturing could eventually help to alleviate this problem by allowing the fabrication of magnets with complex geometries consolidated on electric motor parts. Furthermore, another aspect to increase the electric motors’ efficiency is the possibility to operate at higher RPM and with higher electrical currents, consequently generating more heat. Currently, most magnets are prepared with NdFeB, which is less tolerant to high-temperature exposure. This work reports on the cold spray additive manufacturing of samarium-cobalt (SmCo), a material of growing interest since it preserves most of its magnetic properties up to 350 °C. The permanent magnets were fabricated using a SmCo-Al composite powder mix in a standardized simple geometry to evaluate the impact of the fabrication parameters. The impact of the powder mix composition and the gas temperature on the magnetic properties is investigated. The results demonstrate that the use of cold spray would be effective for fabricating SmCo composite permanent magnets directly on the electric motor parts.
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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".