Cam-profile optimization by means of undercutting in cam-roller speed reducers
Bibliographic record
Abstract
Speed-o-Cam, a family of speed-reduction mechanisms based on cams and pure-rolling contact, is currently under development at McGill University's Centre for Intelligent Machines. This family is intended to replace gears and harmonic drives in applications where backlash, friction, and flexibility cannot be tolerated. In this thesis, we focus on the internal and external planar Speed-o-Cam with both positive and negative actions. We introduce 2-4-6 and 2-4-6-8 polynomials to modify the cam profile around both the cusp and the blunt point of the profile to improve the cam dynamic and kinematic performance. In a third approach, we resort to a cubic spline to solve the same problem. We pay special attention to the curvature of the cam, especially in connection with its machinability. To this end, we resort to Fourier analysis and, thus, propose the concept of loss of geometric regularity, which measures curvature changes. Then, we propose one more concept, the effective machinability of a cam. Both machinability and effective machinability vary from 0% to 100%. We also study the pressure-angle distribution and derive the relationships between the pressure angle and the parameters of the cam. A method to decrease the pressure angle under negative action is introduced. Moreover, an application of Speed-o-Cam to the design of the driving units of wheeled mobile robots is reported in the thesis.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".