Comparative Analysis for Low-Mass and Low-Inertia Dynamic Balancing of Mechanisms
Bibliographic record
Abstract
Dynamic balance is an important feature of high speed mechanisms and robotics that need to minimize vibrations of the base. The main disadvantage of dynamic balancing, however, is that it is accompanied with a considerable increase in mass and inertia. Aiming at low-mass and low-inertia dynamic balancing, in this article the relative importance of the balance parameters of common balancing principles is analyzed and the balancing principles are compared. To do this, the evaluation of a balanced rotatable link is found to be representative for a large group of balanced mechanisms. Therefore, a rotatable link is balanced with duplicate mechanisms (DM), with a countermass (CM) and a separate counter-rotation (SCR), and with a counter-rotary countermass (CRCM). The equations for the total mass and the inertia are derived and compared analytically while the balancing principles are compared numerically. The results show that the DM-balanced link is the best compromise for low mass and low inertia but requires a considerable space. For the CRCM-balanced link and the SCR-balanced link that are more compact, there is a trade-off between mass and inertia for which the CRCM-balanced link is the better of the two.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".