EXPERIMENTAL IDENTIFICATION OF CONFIGURATION DEPENDENT LINKAGE VIBRATION IN A PARALLEL ROBOT USING SMART MATERIAL ACTUATORS AND SENSORS
Bibliographic record
Abstract
A new lightweight planar parallel robot is designed to achieve high acceleration and execute high-speed pick and place tasks. However, due to the lightweight structure of the system, unwanted structural vibrations are induced during motion of the platform. This work focuses on the investigation of the characteristics of this structural vibration utilizing distributed Lead Zirconate Titanate (PZT) transducers. Experimental Modal Analysis (EMA) tests were performed on an experimental three degree of freedom planar parallel robot with one flexible linkage, for cases in which the robot is stationary and in motion. For both cases, configuration dependency of link structural vibration is investigated by performing EMA in different configurations using different combinations of transducers. It is observed that FRFs obtained for the case in which the robot is in motion have more complex frequency compositions and more - significant configuration-dependency than those in which the robot is stationary. However, the two groups of analyses exhibit two nearly identical most-significant modes. Based on this observation, the transfer function from motor inputs to linkage vibration is simplified permitting an linear active vibration controller to be used.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".