Analysis of the Kinematic Accuracy Reliability of a 3-DOF Parallel Robot Manipulator
Bibliographic record
Abstract
Kinematic accuracy reliability is an important performance index in the evaluation of mechanism quality. By using a 3-DOF 3-PUU parallel robot manipulator as the research object, the position and orientation error model was derived by mapping the relation between the input and output of the mechanism. Three error sensitivity indexes that evaluate the kinematic accuracy of the parallel robot manipulator were obtained by adapting the singular value decomposition of the error translation matrix. Considering the influence of controllable and uncontrollable factors on the kinematic accuracy, the mathematical model of reliability based on random probability was employed. The measurement and calculation method for the evaluation of the mechanism's kinematic reliability level was also provided. By analysing the mechanism's errors and reliability, the law of surface error sensitivity for the location and structure parameters was obtained. The kinematic reliability of the parallel robot manipulator was statistically computed on the basis of the Monte Carlo simulation method. The reliability analysis of kinematic accuracy provides a theoretical basis for design optimization and error compensation.
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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 | 0.002 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".