Optimal design of a lightweight robotic manipulator using carbon fibre-reinforced composites
Bibliographic record
Abstract
One of the most attractive applications of composite materials is in robot industry. The reason lies in the fact that lightweight composite materials with excellent performance can dramatically overcome the tricky conflict of simultaneously achieving high end-effector positional accuracy and high link acceleration. This thesis describes the process of analysis and redesign of an anthropomorphic parallel robotic manipulator using graphite/epoxy fiber reinforced composites, which exhibit high stiffness-to-weight ratio and strength-to-weight ratio as well as good damping properties. From the structural viewpoint, by means of finite element analysis, the research into the composite robot arms covers the following aspects: redesign and shape optimization of the robot arms using shell structures; optimizing stacking sequence and fiber orientations for composite laminates; incorporating metal inserts into composite structures to improve local stress concentrations and modal analysis to ensure the high dynamic characteristics of the newly developed structures. Comparing with the original design using metal links, the improved composite counterpart significantly increased the stiffness of the robot arm while decreasing their mass and inertia to achieve a very high specific stiffness, specific strength and excellent dynamic performance.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".