Data ‐Driven Long‐Term Energy Efficiency Prediction of Dielectric Elastomer Artificial Muscles
Bibliographic record
Abstract
Abstract The widespread adoption of dielectric elastomer actuators (DEAs) as compliant artificial muscle is on the horizon after a wide range of prototype demonstrations. The next step is to accelerate the material optimization for specific design requirements. This work proposes a data‐driven framework to predict long‐term DEA energy consumption and efficiency using measurements of initial electrical properties. DEA datasets are generated from 242 pre‐stretched single‐layer actuators and 53 multi‐layer bending actuators actuated for 30–180min. Devices are made with different elastomer and electrode materials, and tested with a novel instrument that can measure multiple aspects of DEA actuation. First, experiments are conducted to develop an empirical understanding of the electro‐mechanical energy conversion mechanism and the impact of material choices during DEA actuation. Second, data‐driven models are applied to the datasets to predict energy consumption and efficiency. Third, the potential generalization of this approach is investigated by using transfer learning strategies to predict energy efficiency at 100 min with as little as 1 min of input data. Moreover, it is found that linear regression can extend the prediction up to 180 min. Additionally, transfer learning is applied to predict energy‐related properties of multi‐layer DEAs using a small dataset. The proposed data‐driven framework can evolve into an intelligent system for accelerating new material discovery by predicting performance and providing device optimization strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".