Tendon-Driven Functionally Gradient Soft Robotic Gripper 3D Printed with Intermixed Extrudate of Hard and Soft Thermoplastics
Bibliographic record
Abstract
Abstract Fused deposition modeling (FDM) printers are some of the most common additive manufacturing (AM) systems in use today. One of their most significant drawbacks compared with alternative AM techniques is that they are unable to easily integrate multiple materials into a single process to produce gradient parts, which have different mechanical properties within a single printed object. Much of this limitation comes from the need to have single filaments as inputs to a printed part, and weak bonding between thermoplastics if they are not chemically miscible. In this work, a simple method to actively enhance the bonding strength between chemically immiscible thermoplastics using a static intermixer inserted into the nozzle of a multi-input FDM system has been demonstrated. This system was successfully used to enhance adhesion between rigid and soft, stretchable polymers, which have nearly three orders of magnitude of difference in elastic moduli. The replaceable intermixer within the print head permits direct comparison of side-by-side or intermixed coextrusion processes. The bond strength between adjacent deposited fibers in intermixed printing was found to be at least 12 times higher than that of the fibers in side-by-side printing. As a proof-of-concept, tendon-driven soft robotic fingers with functionally gradient materials produced from mechanically interlocked dissimilar polymers have been printed and characterized. The fingers printed with intermixed coextrusion of hard and soft polymers do not show any noticeable interface failure after 10,000 cycles of operation, whereas other samples printed with side-by-side coextrusion experienced layer delamination before 10,000 cycles. By using a two-tendon system, these fingers have an agonist–antagonist balanced structure to control its stiffness during operation. The soft robotic gripper fabricated from these printed fingers shows its capability to grasp irregular objects with sizes larger than the gripper holder by actuating in both inward and outward directions.
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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".