Magnetic materials fabricated by cold spray additive manufacturing for the next generation of electric motors
Bibliographic record
Abstract
Additive manufacturing is nowadays established as a sound solution to fabricate complex-shaped parts with enhanced performance and functionalities as indicated by a strong industrial interest. Complex-shaped magnets are of great interest for electric motor manufacturing as it could open the way for innovative solutions for higher power density and better efficiency. Cold spray additive manufacturing allows the fabrication of complex 3D configurations of permanent magnets and soft magnetic materials without the need for assembly and with better mechanical properties than what can be obtained by conventional techniques and other additive manufacturing techniques. New developments on the use of cold spray additive manufacturing for the fabrication of permanent magnets and soft magnetic materials will be presented via a detailed description and analysis of the hard and soft magnetic materials selection, the process optimization (temperature, feedstock properties, magnetic loading) and the fundamental characterization results of magnetic properties (remanence and coercive field) and mechanical properties (adhesion, tensile). Improvement of electric motor design via the use of 3D shaped magnets will be illustrated through examples that were simulated using finite element analysis. Potential gains in terms of torque, torque density, torque ripple management and efficiency will be highlighted. Fabrication of relevant configurations for the automotive and aerospace industries will be illustrated via demonstrator parts realized using cold spray additive manufacturing incorporating complex robot toolpath programming.
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 | 0.001 | 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".