A Piezoelectrically-Actuated Mesoscale Compliant Parallel Robot via Additive Manufacture
Bibliographic record
Abstract
Micro-positioning and pick-and-place applications at the millimeter scale are driving the development of smaller robots necessitating the use of alternative methods for design and manufacture. Additive manufacturing can enable significant cost and time savings in the fabrication of robots while having a low barrier to entry. Specifically, multimaterial 3D printing naturally lends itself to the creation of monolithic mechanisms by removing the requirement for manual assembly, in particular, when compliant joints can replace the rigid joints that are traditionally used. The lack of an assembly requirement naturally opens up the possibility of reducing the size scale of these mechanisms. In this work, the design, fabrication, and characterization of an additively manufactured mesoscale compliant parallel robot actuated by piezoelectric bimorphs through a compliant transmission mechanism is presented. The transmission mechanism is required to convert and amplify the small but rapid linear displacements of piezoelectric actuators into the large rotational motion that is required to create a large workspace for the compliant parallel robot. The developed planar parallel robot has a workspace with maximum planar extents of 14.36 mm by 8.66 mm, with a total area of 65.6 mm2. Three different trajectories are tracked at frequencies of up to 10 Hz, demonstrating the robot's capability to rapidly follow trajectories in its workspace.
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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 | 0.001 | 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".