Soft pneumatic actuators with integrated resistive sensors enabled by multi-material 3D printing
Bibliographic record
Abstract
Abstract The concept of soft robots has garnered significant attention in recent studies due to their unique capability to interact effectively with the surrounding environment. However, as the number of innovative soft pneumatic actuators (SPAs) continues to rise, integrating traditional sensors becomes challenging due to the complex and unrestricted movements exhibited by SPA during their operation. This article explores the importance of utilising one-shot multi-material 3D printing to integrate soft force and bending sensors into SPAs. It highlights the necessity of a well-tuned and robust low-cost fabrication process to ensure the functionality of these sensors over an extended period. Fused deposition modelling (FDM) offers a cost-effective solution for embedding sensors in soft robots, directly addressing such necessity. Also, a finite element method (FEM) based on the nonlinear hyper-elastic constitutive model equipped with experimental input is developed to precisely predict the deformation and tip force of the actuators measured in experiments. The dynamic mechanical test is conducted to observe and analyse the behaviour and resistance changes of conductive thermoplastic polyurethane (CTPU) and varioShore TPU (VTPU) during a cyclic test. The flexible sensor can detect deformations in SPAs through the application of air pressure. Similarly, the force sensor exhibits the ability to detect grasping objects by detecting changes in resistance. These findings suggest that the resistance change corresponds directly to the magnitude of the mechanical stimuli applied. Thus, the device shows potential for functioning as a resistive sensor for soft actuation. Furthermore, these findings highlight the significant potential of 3D and 4D printing technology in one-shot fabrication of soft sensor-actuator robotic systems, suggesting promising applications in various fields like grippers with sensors and rehabilitation devices.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".