Type Synthesis of 3-DOF PPR Parallel Manipulators Based on Screw Theory and the Concept of Virtual Chain
Bibliographic record
Abstract
PPR-PMs (parallel manipulators) are the parallel counterparts of the 3-DOF PPR serial robots, which are composed of two P (prismatic) joints and one R (revolute) joint. For a PPR-PM, the moving platform can rotate arbitrarily about an axis undergoing a planar translation. This paper deals with the type synthesis of 3-DOF PPR-PMs. At first, virtual chains are introduced to represent the motion patterns of 3-DOF motions and relevant results from screw theory are recalled. A method is then proposed for the type synthesis of 3-DOF PPR-PMs. Using the proposed approach, the type synthesis of 3-DOF PPR-PMs is performed in three steps, namely, the type synthesis of legs for PPR-PKCs (parallel kinematic chains), the type synthesis of PPR-PKCs, and the selection of actuated joints of PPR-PMs. The three steps are dealt with in detail consequently. The characteristics of the proposed approach is that the type synthesis of legs for PPR-PKCs is reduced to the type synthesis of 3-DOF overconstrained single-loop kinematic chains and thus easy to perform. In addition to all the 3-DOF PPR-PKCs and 3-DOF PPR-PMs proposed in the literature, a number of new 3-DOF PPR-PKCs and 3-DOF PPR-PMs are identified. It is also found that there are no PPR-PMs with identical types of legs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".