Enhanced Displacement Magnification in Symmetrical Differential Levers: A Compliant Mechanism Design Optimization Study
Bibliographic record
Abstract
Compliant mechanism work based on the elasticity of material, dimension of the compliant mechanisms and the shape of flexure hinge.In order to larger workspace, most published works use theoretical models to determine the displacement amplification of the mechanical systems, which is very difficult to do.A simpler method that can still achieve efficiency while designing a mechanism with high displacement gain and low stress that ensures a stable working structure is to use combined grey relational analysis Taguchi method is based on the results of finite element analysis in ANSYS.To do this, first select the design variables for the symmetric differential lever displacement amplifier model.Next step, use Minitab software to design 27 cases.Then use SolidWorks to design 27 models of symmetrical differential displacement amplifier.Next to finite element analysis in ANSYS to obtain displacements and stresses of the symmetrical differential lever compliant mechanism with circular flexure hinge.The results obtained from the finite element model are used for optimization by grey relationship analysis combined with the Taguchi method.The FEM results indicated that the designed variables significantly affected on the displacement and stress of the symmetrical differential lever displacement magnification compliant mechanism.The problem was also confirmed by grey relational analysis with Taguchi method.The predicted and optimal values of the displacement were 0.11276 mm and 0.1179 mm, with error of 4.36%.The input displacement was 0.01 mm, while the displacement magnification ratio was 11.79 times.The results verified by decision-making criteria: TOPSIS method, MOORA method and EDAS method.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".