Design of Parallel Mechanisms for Flexible Manufacturing With Reconfigurable Dynamics
Bibliographic record
Abstract
Reconfigurable robotic systems can enhance productivity and save costs in the ever growing flexible manufacturing regime. In this work, the idea to synthesize robotic mechanisms with dynamic properties that are reconfigurable is studied, and a methodology to design reconfigurable mechanisms with this property is proposed, named reconfigurable dynamics (Re-Dyn). The resulting designs have not only the kinematic properties reconfigurable, such as link lengths, but also properties that directly affect the forces and accelerations, such as masses and inertias. A 2-degree of freedom (DOF) parallel robot is used as a test subject. It is analyzed and redesigned with Re-Dyn. This work also presents the robots forward dynamic model in detail, which includes the force balancing mediums. The connection method is directly utilized for this derivation, which is well suited for multibody dynamics and provides insight for design parameters (DPs). Dynamic performance indices are also briefly discussed as related to the Re-Dyn method. After redesigning the robot, a full simulation is conducted to compare performances related to a flexible manufacturing situation. This illustrates the advantages of the proposed method.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".