Direct Assembly of Multilayered Core–Shell Structured Composites for Self‐Sensing and Soft Robotic Fingers
Bibliographic record
Abstract
Abstract The growing demand for intelligent, adaptive gripping in soft robotics has driven interest in advanced manufacturing techniques for self‐sensing, stiffness‐programmable composites. Traditional fabrication methods often involve complex multi‐step processes, limiting efficiency and functionality. In this work, a novel core‐programmable coaxial direct ink writing (CPC‐DIW) technique is presented for the rapid, one‐step fabrication of soft robotic fingers with programmable stiffness and integrated self‐sensing capabilities. The basic structure consists of a lower‐modulus silicone core encapsulated in a higher‐modulus silicone shell, forming coaxial fibers that provide tunable stiffness. Conductive silicone alternatively embedded within the fiber's core enables real‐time strain sensing without the need for external sensors. This seamless integration of actuation and sensing components eliminates mechanical mismatches, enhancing durability and functionality. Finite element analysis and experimental results confirm the soft fingers’ precise, adaptive control, demonstrating the potential of CPC‐DIW for direct manufacturing of multifunctional soft robotic systems. This approach provides an effective strategy for manufacturing intelligent soft grippers with precise control and unprecedented versatility.
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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".