Design and Optimization of Revolute Flexure Joints for Compliant Parallel Mechanisms
Bibliographic record
Abstract
Flexure joints are widely incorporated in a large number of precise applications such as micro-positioning stages and high-accuracy alignment instruments due to their monolithic character. As the main constituents of compliant mechanisms, the joint characters can influence the static and dynamic performances of the overall mechanism. This paper describes the Simulation-Driven Design and Optimization (SDDO) of the revolute flexure joint with high performance. By the implementation of the SDDO, the designed joint possesses high performance such as high precision of output motion, low energy consumption, large capacity of rotation, high stiffness, simultaneously. A prismatic flexure joint and a universal flexure joint constructed of designed joints are included to illustrate the analysis and design techniques. The presented design can be usefully used in other parallel compliant mechanisms and the SDDO method can also be used to develop industrial products.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 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".