An end-effector for polishing based on a parallel mechanism with 3-PSS + PS′
Bibliographic record
Abstract
To enable a polishing tool to automatically adjust its posture and adapt to the surface of the workpiece, an end-effector based on a parallel mechanism with 3-PSS + PS′ (prismatic–spherical–spherical + prismatic–sphere–pin) was designed by using the reverse adaptive working principle. Based on the spiral theory, the degrees of freedom (DOF) of the mobile platform in the parallel mechanism are analyzed, and its DOF are verified according to the modified Grübler–Kutzbach formula. According to the kinematic analysis, the relationship between the plunger movement distance and the grinding wheel spatial pose is determined. The polishing force function between the grinding wheel and workpiece is obtained according to the analysis of the spatial pose of the grinding wheel. The main structural dimensions of the model were determined, and the physical model was given. The simulation analysis of the designed structure is carried out; the results show that the end-effector can realize 3-DOF motion. The maximum rotation angle of the grinding wheel around the x-axis and y-axis is 7.68° and 6.68°, respectively, which meets the design requirements. Within the working range, without controlling the end of a manipulator, the grinding wheel can adaptively compensate the trajectory error, and for different materials 45 steel, HT-200, 6061, the polishing force fluctuation is within 2.56% after stable polishing, which is basically constant.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".