A simple and efficient dynamic modeling method for compliant micropositioning mechanisms using flexure hinges
Bibliographic record
Abstract
In this paper we consider the dynamic modelling of compliant micropositioning mechanisms using flexure hinges. A simple modelling method is presented that is particularly useful for modelling parallel micropositioning mechanisms. This method is based upon linearisation of the geometric constraint equations of the compliant mechanism. This results in a linear kinematic model, a constant Jacobian and linear dynamic model. To demonstrate the computational simplicity of this methodology it is applied to a four-bar linkage using flexure hinges. Comparisons are made between the simple dynamic model and a complete non-linear model derived using the Lagrangian method. The investigation reveals that this new model is accurate yet computationally efficient and simple to use. The method is then further applied to a parallel 3-degree of freedom (dof) mechanism. It is shown that the method can be simply applied to this more complex parallel mechanism. A dynamic model of this mechanism is desired for use in optimal design and for controller design.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".