Improving the Cooling Time of Twisted Coiled Actuators in Soft Robotics
Bibliographic record
Abstract
Fabric cooling channels for twisted coiled actuators (TCAs) were recently proposed to achieve the required response times for motion assistance in a manner suitable for soft wearable robotic devices. While previous work demonstrated that the fabric channel reduced the cooling time by 42% in comparison to the same TCA without the cooling channel, the TCAs were still not cooled quickly enough to support human motion. Therefore, in this paper, two variations to the channel are proposed to further reduce the cooling time of the TCAs. The variations include unsealing the inlet and adding vents along the length of the channel to take advantage of air entrainment and natural convection. While both variations reduced the cooling time on their own, when they were employed together there was a 34% reduction in cooling time compared to the original channel design ($19.1 \pm 2.4\ \mathrm{s}$vs.$13.5 \pm 0.9\ \mathrm{s}, \mathrm{p} < 0.001$). This decrease occurred without any significant differences in the stroke or heating time of the TCA. The modified channel was then compared to the TCA without the cooling apparatus and the cooling time was reduced by 57% ($25.1 \pm 1.7\ \mathrm{s}$vs.$14.0 \pm 1.2\ \mathrm{s}, \mathrm{p} < 0.001)$. This work advances the development of a cooling system for TCAs, making it suitable for soft wearable robotic devices by improving portability, and thereby enabling their use in wearable devices for rehabilitation applications.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".