Bibliographic record
Abstract
Cam mechanisms are widely used in industry, in applications requiring quick-return and indexing motions. A current research effort at the Robotic Mechanical Systems Laboratory of McGill University's Centre for Intelligent Machines aims at the application of cam mechanisms as speed reducers. The accuracy required in these mechanisms is of the utmost importance, especially when cams are rotating at a high speed. In this thesis, the design for manufacturability of planar speed-reduction cam mechanisms is studied. In particular, the thesis focuses on a speed reducer with a rotating follower to couple shafts of parallel axes, termed planar Speed-o-Cam. Principles of the design for manufacturability are applied to Speed-o-Cam and a unified method for obtaining the optimum parameters satisfying the curvature constraints and pressure-angle bounds is developed. These two factors are relatively important because Numerically Controlled and Computer Numerically Controlled machine tools could be very sensitive to changes of curvature of the workpiece, especially when milling complex shapes such as those of cam plates. Cam-mechanism balancing is also studied because unbalance in a high-speed rotating element can cause severe vibrations and greatly affect the bearings and hence, the performance of the machine. This is done by not only adding counterweights, which unavoidably increase the weight and volume of the mechanism, but also by removing material.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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".