Shaking Force and Shaking Moment Balancing in Planar Serial Manipulators
Bibliographic record
Abstract
High-speed mechanical systems are recognized as key sources of vibration excitation.Fast-moving manipulators, in particular, can generate substantial fluctuating forces and moments.As a result, balancing shaking forces and shaking moments, which arise from the inertial forces of the links, becomes critically important.The quality of mass balancing not only affects vibration levels but also influences the lifespan, reliability, and accuracy of manipulators.In addition to the negative effects mentioned, vibrations contribute to environmental pollution, energy loss, and may lead to various health issues.Therefore, improving mass balancing quality holds not only technical, technological, and economic significance but also social implications.This paper discusses the complete balancing of shaking forces and moments in planar serial manipulators.To achieve this, a combination of two approaches is utilized.First, dynamic decoupling and linearization of the motion equations are performed.Following this, the shaking forces acting on the frame become constant and equal to the sum of the gravitational forces of the links, while the shaking moment becomes proportional to the acceleration of the first link attached to the frame.This enables the addition of a pair of identical gears, mounted on the manipulator's frame and first link, to cancel the shaking moment.The proposed solution is demonstrated using a planar two-degree-of-freedom serial manipulator and validated through numerical simulations conducted with ADAMS software.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".