Dimensional synthesis of motion generation in a spherical four-bar mechanism
Bibliographic record
Abstract
A new objective function for optimizing spherical, four-bar mechanisms for motion generation is presented. Rigid-body poses of a spherical four-bar mechanism in a standard installation position are investigated, and a normalization processing method is proposed. After processing the coupler points of the mechanism, the feature points generated are located on the circles. The formation principle of the feature coupler circles is analysed, and the internal relationship between the centre angle of the adjacent feature points on the feature coupler circles and the coupler angle of the spherical four-bar mechanism is determined. An input angle determination method is proposed for the spherical four-bar mechanism in a general installation position. An objective function is then established to optimize the basic dimensional types, relative input angles, and installation position parameters of the desired spherical four-bar mechanism. The proposed method is applicable to both prescribed and unprescribed timing problems. Notably, the dimension of the optimization variables is only eight, which is exceptionally low for multiple position motion generation without prescribed timing; therefore, the global optimal solution is more easily obtained. The optimization process is carried out by the genetic algorithm. The feasibility and effectiveness of our proposed method are demonstrated by examples.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".