Interactions and Optimizations Analysis between Stiffness and Workspace of 3-UPU Robotic Mechanism
Bibliographic record
Abstract
Abstract The interactions between stiffness and workspace performances are studied. The stiffness in x, y and z directions as well as the workspace of a 3-UPU mechanism are studied and optimized. The stiffness of the robotic system in every single moveable direction is measured and analyzed, and it is observed that in the case where one tries to make the x and y translational stiffness larger, the z directional stiffness will be reduced, i.e. the x and y translational stiffness contradicts with the one in z direction. Subsequently, the objective functions for the summation of the x and y translational stiffness and z directional stiffness are established and they are being optimized simultaneously. However, we later found that these two objectives are not in the same scale; a normalization of the objectives is thus taken into consideration. Meanwhile, the robotic system’s workspace is studied and optimized. Through comparing the stiffness landscape and the workspace volume landscape, it is also observed that the z translational stiffness shows the same changing tendency with the workspace volume’s changing tendency while the x and y translational stiffness shows the opposite changing tendency compared to the workspace volume’s. Via employing the Pareto front theory and differential evolution, the summation of the x and y translational stiffness and the volume of the workspace are being simultaneously optimized. Finally, the mechanism is employed to synthesize an exercise-walking machine for stroke patients.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".