Fabrication and electromechanical examination of a spherical dielectric elastomer actuator
Bibliographic record
Abstract
In this paper, a procedure for fabricating and testing a seamless spherical dielectric elastomer actuator (DEA) is presented. In previously developed spherical prototypes, the DEA material is pre-strained by a rigid frame to improve the actuator's output force; however, it is possible to pre-strain a spherical DEA by inflating the sample with a liquid or gas as long as the sample contains the pressure. In this work, a very compliant silicone-based material was used to fabricate a nearly spherical balloon-shaped prototype. The DEA sample was inflated by air and various electrical-actuation regimes were considered. The performance of the DEA sample was studied using an analytical and a finite element-based model. An Ogden hyperelastic model was used in formulation of the analytical model to include nonlinear behavior of the silicone material. Full statistical analysis of the experimental and numerical results was carried out using the root-mean-square (RMS) error and the normalized RMS error. The analytical and FEM results were in good agreement with the experimental data. According to modeling results, it was found that the DEA's actuation force can be mainly improved by increasing the voltage, reducing the thickness, lowering the stiffness, and/or increasing the initial pressure. As an example, a three-fold increase of the actuation force was found when the thickness was reduced to half of its initial value. This improvement of the efficiency suggests that the spherical DEA is suitable for use in several applications if an appropriate design with optimal governing parameters is developed.
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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) | 0.000 | 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".