Workspace analysis and optimal design of a 3-leg 6-DOF parallel platform mechanism
Bibliographic record
Abstract
A new class of six-degree-of-freedom (DOFs) spatial parallel platform mechanism is considered in this paper. The architecture consists of a mobile platform connected to the base by three identical kinematic chains using five-bar linkages. Recent investigations showed that parallel mechanisms with such a topology for the legs can be efficiently statically balanced using only light elastic elements. This paper follows up with a workspace analysis and optimization of the design of that parallel mechanism. More specifically, considering a possible industrial application of the architecture as a positioning and orienting device of heavy loads, an optimization procedure for the maximization of the volume of the three-dimensional (3-D) constant-orientation workspace of the mechanism is first presented. As the mechanism could also have great potential as a motion base for flight simulators, we develop here a discretization method for the computation and graphical representation of a new workspace with coupled translational and rotational DOFs. This workspace can be defined as the 3-D space which can be obtained when generalized coordinates x,y and torsion angle /spl psi/ in the tilt-and-torsion angles parametrization are constant. A second procedure is then presented for the maximization of the volume of this second subset of the complete workspace. For both approaches, our purpose is to attempt an optimal design of the mechanism by maximizing the volume of the associated 3-D Cartesian region that is free of critical singularity loci.
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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".