Topology Optimization of a MEMS Resonator Using Hybrid Fuzzy Techniques
Bibliographic record
Abstract
This paper introduces a new methodology for the design of structures by geometry and topology optimization accounting for loading and boundary conditions as well as material properties. The Fuzzy Heuristic Gradient Projection (FHGP) method is used as a direct search technique for the geometry optimization, while the Complex Method (CM) is used as a random search technique for the topology optimization. In the proposed method, elements are designed such that they all have the same amount of stresses using the Fuzzy Heuristic Gradient Projection method. On the other hand, the complex method is used for the topology optimization step satisfying any constraint other than the stress constraint. The developed hybrid fuzzy technique is applied for different applications ranging from micro-scale to macro-scale applications. The method is applied to a micro-mechanical resonator as a microelectro-mechanical system (MEMS). The resonator is solved for minimum weight and is subjected to an equality frequency constraint and an inequality stress constraint. The proposed method is compared with the Multi-objective Genetic Algorithms (MOGAs) on solving the MEMS resonator. Results showed that the proposed hybrid fuzzy technique converges to optimum solutions faster than (MOGAs). The time consumed is improved by a 77%.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".