Design of Revolute Joint With Bi-Stability Using Permanent Magnets
Bibliographic record
Abstract
Abstract Bi-stable mechanisms are systems with two distinct stable equilibrium positions within their range of operation. They are capable of steadily staying in positions without external power input and require less energy to move to the next stable state because of their snap-through behavior. Diverse applications including switches, deployable structures, and reconfigurable robots can benefit from bi-stability characteristics. However, the complexity of implementation and the limitation of structure configuration have made it difficult to apply conventional bi-stable mechanisms to the structures that require rotational bi-stability. Thus, in this paper, we proposed an implementation method using cylindrical magnets for the rotational bi-stable mechanism. The proposed bi-stable mechanism consists of a revolute joint with two links; one is the rotational link and the other is the fixed link. It has rotational bi-stability through the magnetic force relationship between the array of magnets on each link. To identify the characteristics of the proposed bi-stable mechanism, a cylindrical permanent magnet is considered as an electromagnet model that consists of one ring with a virtual electric current. Consequently, the magnetic field of the cylindrical permanent magnet can be calculated using Biot-Savart law. Similarly, the magnetic force between two cylindrical permanent magnets of the electromagnet model is calculated using Lorentz force law. The criteria of the magnet array for symmetric bi-stability are proposed and the potential energy diagram of the rotation link is considered as the performance criterion to identify the stable state.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".