Automated Fabrication of 3D Printed Magnetic Soft Robots With Programmable 3D Magnetizations
Bibliographic record
Abstract
Magnetic soft robots (MSRs) are a viable tool for many biomedical applications, such as targeted drug delivery and minimally invasive surgery, since they can be actuated remotely using external magnetic fields. These robots are developed by programming ferromagnetic domains with specific magnetizations using magnetic particles embedded in a flexible substrate. Existing fabrication methods rely on partially automated or manual processes, which limit production rates and realistic design iterations. To address these challenges, a fully automated workflow that translates robot simulations into an instruction set for a stereolithography 3D printer is presented. In this process, a rotating permanent magnet is used to program 3D magnetizations by reorienting hard magnetic particles within a photosensitive resin. Geometric resolutions of 1.6 mm are achieved with a layer height of 0.1 mm, enabling the creation of structures 14 layers thick. Beam bending tests identify an optimal 6:1 resin‐to‐magnetic particle mass ratio, yielding a maximum deflection angle of 80°. Demonstrated applications include rolling and climbing locomotion in a maze and independent control of each arm in a multiarmed robot. By enabling fast, repeatable production of MSRs within 30 min, this automated system shortens the feedback loop from design to application, advancing their potential as a biomedical tool.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.002 |
| 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) | 0.000 | 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 teacher head, 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".