Toward a Unified Design Approach for Both Compliant Mechanisms and Rigid-Body Mechanisms: Module Optimization
Bibliographic record
Abstract
Rigid-body mechanisms (RBMs) and compliant mechanisms (CMs) are traditionally treated in significantly different ways. In this paper, we present a synthesis approach that is appropriate for both RBMs and CMs. In this approach, RBMs and CMs are generalized into modularized mechanisms that consist of five basic modules, including compliant links (CLs), rigid links (RLs), pin joints (PJs), compliant joints (CJs), and rigid joints (RJs). The link modules and joint modules are modeled through beam elements and hinge elements, respectively, in a geometrically nonlinear finite-element solver, and subsequently a beam-hinge ground structure model is proposed. Based on this new model, a link and joint determination approach—module optimization—is developed for the type and dimensional synthesis of both RBMs and CMs. In the module optimization approach, the states (both presence or absence and sizes) of joints and links are all design variables, and one may obtain an RBM, a partially CM, or a fully CM for a given mechanical task. Three design examples of path generators are used to demonstrate the effectiveness of the proposed approach to the type and dimensional synthesis of RBMs and CMs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".