Design of Electric Machines Based on Additively Manufactured Lattice Structures
Bibliographic record
Abstract
Recent developments in additive manufacturing (AM) bring about unprecedented potentials in designing and developing electromagnetic components; this paper presents a novel concept for designing magnetic cores of electric machines based on triply periodic minimal surface (TPMS) lattices for the development of high specific power and high efficiency electric machines. Fabrication of TPMS lattices is enabled by and uniquely well-suited for additive manufacturing. Compared to additively manufactured bulk cores, lattice cores can improve the performance of electric machines by (a) mitigating eddy current losses and thus total power losses through the presence of large volume of distributed internal voids, and (b) allowing electric machines to be operated at higher speeds and/or torques by relaxing thermal limits through enhanced internal cooling. In the present study, 3D finite element analysis is used to quantify and compare the electromagnetic characteristics of three representative TPMS types with those of a bulk structure. A functionally graded TPMS lattice design, comprising of alternating thin layers of soft magnetic materials and electrical insulating materials, is subsequently proposed to minimize eddy current losses to a level comparable to that of conventional laminated cores over a wide range of operating frequencies. Finally, a conceptual design of the magnetic core of a synchronous reluctance motor (SynRM), i.e., rotor and stator, is presented to illustrate the idea.
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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".