Critical review on complete dynamic balancing of mechanisms and parallel robots
Bibliographic record
Abstract
When mechanisms and parallel manipulators move, due to the fact that the centre of mass is not fixed and angular momentum is not constant, they often produce vibrations in the base. Shaking force balancing can be achieved by making the centre of mass of mechanism be fixed, i.e. linear momentum is constant; shaking moment balancing can be achieved by making the angular momentum constant. There are generally two main ways for shaking force balancing and shaking moment balancing, i.e. 'balancing before kinematic synthesis' and 'balancing at the end of the design process'. Under the category of balancing at the end of design process, add counterweights and counter-rotations, add ADBU and add auxiliary links are mostly used principles; here a new method is proposed, i.e. balancing through reconfiguration, which can reduce the addition of mass and inertia. Fisher's method belongs to the method of balancing before kinematic synthesis. In this paper, we will discuss the advances and problems on dynamic balancing of mechanisms in detail under the above two main categories. Two main contributions of the paper can be concluded as follows: new reactionless parallel manipulators are derived and dynamic balancing through reconfiguration concept is proposed in first time.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".