Vibration Analysis of a 5-DOF Long-Reach Robotic Arm
Bibliographic record
Abstract
In this paper, dynamic and vibration characteristics of a newly developed 5-degrees-of-freedom (5-DOF) long-reach robotic arm for farm applications is studied through finite element analysis (FEA), as well as experimentally. The new manipulator is designed to be light and compact enough that it can be mounted on a small vehicle for farm applications. A finite element model of this novel manipulator was established using a commercial FEA software. FEA was carried out for two different configurations of the manipulator (fully-extended and vertical half-extended). The fully-extended configuration provides the longest reach of the arm and is one of the most commonly used poses in farm applications; vibrations of this configuration are highly affected by its base excitation. The FEA results indicated that the first six natural frequencies of the manipulator for the two configurations considered were between 4.4 to 41.6 (Hz). Modal analysis on the fully-extended configuration was completed using experimental modal analysis to verify the finite element results. In the experiments, acceleration data were obtained utilizing sensors, and were post-processed using Fast-Fourier Transforms. The first six natural frequencies and their corresponding mode shapes were obtained using FEA and also experimentally, and the results were compared; the comparison showed good agreement, with less than 10% difference. Our verified FE model provides a reliable basis for future vibration control for the newly developed robotic arm for different applications. A harmonic response simulation was also carried out using an experimentally corrected FE model; this provides a good understanding of the dynamic behavior of the newly developed arm under base excitation. This paper offers an experimentally corrected FEA model for a large manipulator with base excitation for farm applications.
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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 | 0.000 | 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".