Magnetically controlled bio-inspired elastomeric actuators with high mechanical energy storage
Bibliographic record
Abstract
Many biological systems are made to operate more quickly, efficiently, and with more power by storing elastic energy. This work introduces a straightforward bioinspired design for the quick manufacture of pre-stressed soft magnetic actuators. The actuator requires a lower magnetic field strength to be activated and can regain its original shape without the need for external stimuli. These characteristics are demonstrated in this work through the creation of actuators with round and helical shape structures inspired by the tendril plant and chameleon's tongue. Both the final form of the actuator and its actuation sequence may be programmed by controlling the direction and strength of the force utilised to pre-stress the elastomeric layer. Analytical models are presented to trace the actuators' energy storage, radius, and pitch. High-speed shape recovery after releasing the magnetic force and a strong grasping force are achieved due to the stored mechanical elastic energy. Experiments are conducted to analyse the shape changes, grasping action, and determine the actuation force. The manufacture of the grippers with zero-magnetic field strength holding capacities of up to 20 times their weight is made possible by the elastic energy that actuators store in their pre-stressed elastomeric layer. The outcomes of our research show that a unique magnetic field-controlled soft actuator can be created in different shapes and designs based on requirements.
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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".