A study of the free vibration of flexible-link flexible-joint manipulators
Bibliographic record
Abstract
In this article, explicit expressions for the frequency equation, mode shapes, and orthogonality of the mode shapes of a Single Flexible-link Flexible-joint manipulator (SFF) are presented. These explicit expressions are derived in terms of non-dimensional parameters which make them suitable for a sensitivity study; sensitivity study addresses the degree of dependence of the system’s characteristics to each of the parameters. The SFF carries a payload which has both mass and mass moment of inertia. Hence, the closed-form expressions incorporate the effect of payload mass and its mass moment of inertia, that is, the payload mass and its size. To check the accuracy of the derived analytical expressions, the results from these analytical expressions were compared with those obtained from the finite element method. These comparisons showed excellent agreement. By using the closed-form frequency equation presented in this article, a study on the changes in the natural frequencies due to the changes in the joint stiffness is performed. An upper limit for the joint stiffness of a SFF is established such that for the joint stiffness above this limit, the natural frequencies of a SFF are very close to those of its flexible-link rigid-joint counterpart. Therefore, the value of this limit can be used to distinguish a SFF from its flexible-link rigid-joint manipulator counterpart. The findings presented in this article enhance the accuracy and time-efficiency of the dynamic modeling of flexible-link flexible-joint manipulators. These findings also improve the performance of model-based controllers, as the more accurate the dynamic model, the better the performance of the model-based controllers.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".