Development of a Magnetic Levitation System for Additive Manufacturing: Simulation Analyses
Bibliographic record
Abstract
Magnetic levitation has been always a very promising field. Due to its versatility, it has garnered interest in field energy harvesting, high speed transportation, and high precision micro-manipulation amongst other fields. However, its application within the sphere of additive manufacturing (AM) has been negligible. Thus, for the first time, the work conducted here works with the development of a novel technique utilizing magnetic levitation for stable suspension for AM. Current AM techniques are heavily dependent on building the part of a substrate. The substrate limits the use of multiple material nozzles and also requires significant post-processing. The use of the principle of magnetic levitation can bypass the need for this substrate. Primary emphasis is placed on the computation of eddy currents and Lorentz force produced from the magnetic levitation system, and also on finding the relevant parameters affecting several output parameters such as position of levitation, force experienced by the object suspended, etc. A temperature analysis is also conducted to ensure that the principles used here are valid. ANSYS Ansoft Maxwell modules are used to determine these input parameters. The work is subsequently validated through the transient module of the same module. The results presented here depict the viability of the system within the AM environment and the successful elimination of the need for a substrate.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".