Design and characterization of a miniaturized low inertia and low viscous friction magnetorheological clutch using 3D metal printing for human-robot applications
Bibliographic record
Abstract
Robotic actuators such as geared MR actuators must improve their torque capacities and reduce their size to increase system integration density. MR clutches are at the heart of geared MR actuators and conventional machining is a major hurdle to downsizing because it requires having close tolerance for machining and assembling a large number of small parts. This paper studies the potential of nested 3D printed MR clutches to improve the torque density of geared MR actuators at small scales. Nested 3D printed MR clutches are multi-disk MR clutches where the rotor and stator are fabricated simultaneously on a single 3D print. A prototype is designed, built, tested and compared to similar conventionally made MR clutches. The prototype weighs 84 g and can transmit a maximum torque of 0.88 N.m. The fabrication process is fast and simple, and the performance levels well surpass those of comparable machined MR clutches. The manufactured prototype doubles the torque density and multiply respectively by 4 and by 10 the torque-to-inertia and torque-to-viscosity ratios compared to equivalent machined MR clutches. Results show that nested 3D printing of MR clutches is an effective manufacturing process and opens the door to a new generation of high-performance mechanical transducer.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".