Development and Analysis of a Novel Magnetic Levitation System with a Feedback Controller for Additive Manufacturing Applications
Bibliographic record
Abstract
The primary goal of this study is to create a magnetic levitation system for additive manufacturing (AM) applications. The emphasis of this research is placed on Laser Directed Energy Deposition via Powder Feeding (LDED-PF). The primary benefit of using a magnetic levitation system for AM applications is that the levitated geometry is expected to be a portion of the final part manufactured, thus eliminating the need for a substrate and reducing the post-processing operation requirement. Two novel levitation systems were designed, optimized, and manufactured. The design, optimization, and analysis were first conducted in the simulation environment using ANSYS Maxwell and then tested with experiments. The newly developed systems depicted a much-improved performance compared to the first prototype developed in a previous article written by the authors. The newly developed systems had an increase in levitation height, the surface area for powder deposition activities, the time available for AM operations, and the ability to support additional mass within the limits of allowable inputs. The compatibility of the levitation system with AM applications was also verified by testing the impact of powder deposition and the ability of the levitated disc to support added mass as a function of time with minimal loss in performance. This article also highlights the development of a novel feedback PID controller for the levitation system. To improve the overall performance of the controller, a feedforward controller was added in conjunction with the PID controller. Finally, the levitation system was shown to highlight control over levitation height and maintain constant levitation height with the addition of an added mass using the feedback controller.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".