The optimum design of epicyclic trains of spherical cam-roller pairs /
Bibliographic record
Abstract
Many a robotic pitch-roll wrist uses a bevel-gear differential train to drive the gripper. The innovative design of pitch-roll wrists using spherical cam-roller pairs is currently underway at McGill University's Centre for Intelligent Machines, with the aim of overcoming the drawbacks of bevel-gear trains. This innovative design relies on Speed-o-Cam, a new concept of speed-reduction mechanisms based on cams and pure-rolling contact, intended to replace gears and harmonic drives in applications where backlash, friction, and flexibility cannot be tolerated. The new mechanism consists mainly of a spherical conjugate cam subassembly and two roller-carrying disks. We start with a study of cam curvature, with special focus on its machinability. Drawing from experience, we introduce the hypothesis that high curvature changes of a cam profile are at the source of the concentration of machining errors. As a consequence, the machining accuracy of the concave regions in a cam profile is substantially lower than that of its convex regions. To produce a more accurate cam we developed the geometric condition that guarantees a fully convex spherical cam profile. The optimum design of the pitch-roll mechanism based on cam-roller pairs is reported here. The optimization is intended to simplify the subassembly of spherical conjugate cams of the old design by means of a layout of two pairs of spherical mechanisms of the Stephenson type and two conjugate cams mounted on distinct shafts. We focus on the optimum design of both the spherical cam-roller mechanism and the spherical Stephenson mechanism.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".