Modeling of a Fully Flexible 3PRS Manipulator for Vibration Analysis
Bibliographic record
Abstract
In this paper we provide a vibration analysis model and the modeling method for a fully flexible 3-Parallel-Revolute-joint-and-Spherical-joint (3PRS) manipulator—a sliding-leg tripod with flexible links and joints. A series of tripod configurations are set by rigid kinematics for simulation and experiment. All the links are modeled by finite elements: triangular membranes combined with bending plates for the moving platform and spatial beams for the legs. The joint complication is overcome by modeling the joint constraints as virtual springs. The nodal coordinates are statically condensed in order to validate the model. Using eigenvalue sensitivity analysis in terms of the condensed coordinates, the stiffness parameters of the joint virtual springs are adjusted in the experimental configurations until the acceleration frequency response functions (FRFs) from the calculation agree with the ones from the impact tests. The adjusted joint parameters are interpolated linearly into a series of configurations in simulation. The analysis shows that the model with the modified joints proposed in this paper is more effective than the conventional model with ideal joints for predicting the system natural frequencies and their variations against different tripod configurations. The good agreement between the simulation and the experiment at resonant peaks of the FRFs indicates the effectiveness of the modeling method.
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".