Electro-thermo-mechanical microgripper with topology optimized design
Bibliographic record
Abstract
This paper presents the design, finite element modelling, fabrication and performance evaluation of an innovative structure to act as an electro-thermo-mechanical microgripper. The developed microgripper was a micro-electro-mechanical system (MEMS) which couples electrical, thermal and mechanical behaviors to generate tweezing displacements. The actuation principle was based on the electro-thermal effect when the electrical current was converted into heat by Joule's heating causing Topology Optimization Method (TOM) that combines optimization algorithms with the Finite Element Analysis Method (FEM). The optimized microgripper was fabricated from a 25µm thick pure nickel foil using the laser microfabrication technology and its performance was experimentally evaluated using constant current control scheme. The static and dynamic electro-mechanical characteristics were analyzed as step responses with respect to tweezing displacements, applied current/power, and actual resistance. For a microgripper prototype with overall dimensions of 1x2.5 mm, the tweezing displacements of 25.5 µm and 33.2 µm along X and Y axes, respectively, were obtained with an applied power of 2.32 W. Experimental performance was compared with finite element modelling simulation results. These microgrippers may be used in micro-robotics and micro-assembly applications as micro end-effectors for micromanipulating and microhandling operations.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".