Improving the Response Time of a Soft Robotic Gripper Using a Heat Sink with Shape Memory Alloy Actuators
Bibliographic record
Abstract
This study focuses on the time response analysis of a nickel titanium (NiTi) shape memory alloy-based soft robotic gripper with variable stiffness, with a particular emphasis on reducing the cooling time, which will ultimately lead to a faster response time.The manufacturing of the shape memory alloy is not covered in this work.The impact of a heat sink comprising a silicone casing and an ethylene glycol thermal compound on the response time was examined using Finite Element Analysis.The cylindrical cross-section of the gripper's finger was created as a three-dimensional model.Numerical analysis was done using Ansys Workbench, and experimental results validated the numerical results with a 1.12% percentage difference.A 36% overall improvement in response time was observed, indicating that the proposed heat sink is capable of acceleration the rate of heat dissipation from the shape memory alloy, and in turn improves the response time of the gripper.
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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 | 0.000 |
| 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".