Static and Vibration Analyses of a Composite CFRP Robot Manipulator
Bibliographic record
Abstract
This paper reports analyses of a 5-degrees-of-freedom (5-DOF) carbon fiber-reinforced polymer (CFRP) robot manipulator, which has been developed for farm applications. The manipulator was made of aluminum alloy (AA) and steel materials. However, to check the effectiveness of CFRP materials on the static and free-vibration performance of the manipulator, the AA parts were replaced with CFRP. For this purpose, the effects of various cross-sections and layups on three design criteria—deflection, load-carrying capacity, and natural frequency—were investigated. Two types of thin-walled laminated sections, specifically the I section and rectangular tubular sections, were used for the composite parts. These parts were made from three hollow square section (“SSS” section) beams and three I section (“III” section) beams. These multi-cell beams were modeled using the finite element (FE) method. Three configurations were selected for analysis based on the manipulator’s most common operating conditions. The results indicated that the use of CFRP increased the manipulator’s natural frequencies, increased the load-carrying capacity, and decreased the manipulator’s tip deflection when compared with its AA counterpart. An analysis showed that using CFRP in the manipulator’s structure could improve static and vibrational performances. It was observed that the “SSS” section beams were 1.17 times stiffer, could carry a 1.20 times higher load, and were 1.40 times heavier than the “III” section beams. Also, decreasing the fiber direction in angle-ply layups from 90° to 0° and adding 0° plies, while keeping the total number of layers constant, decreased the manipulator’s tip deflection and increased its natural frequencies.
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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".