Understanding and modelling the torsional stiffness of harmonic drives through finite-element method
Bibliographic record
Abstract
Torsional stiffness or rigidity is a crucial characteristic in the design of transmission devices, including harmonic drives (HDs). Among the various design aspects constituting a reduction mechanism in robotic systems, torsional stiffness is an important factor for positioning accuracy and control issues. One of the major advantages of HDs is their capacity to present a high reduction ratio while maintaining a small hardware size. However, manufacturing these drives remains a complex and costly process due to the high precision of its machined components; as a result, the use of such drives is still limited only to high-end mechanical products and technologies. Given these costs, numerical analysis becomes an effective alternative for obtaining valuable data through simulations, without the need for prototypes. This article presents a finite-element model to reproduce the behaviour of the torsional stiffness of an HD. The numerical model allows an evaluation of the effects of various geometrical parameters on the torsional stiffness of the HD. The numerical model of the HD can be used for optimization purposes, i.e. to develop an HD with a high torque capacity combined with a high-rated lifespan.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".