Magnetically-guided in-situ microrobot fabrication
Bibliographic record
Abstract
Mobile microrobots are typically fabricated in a multi-step microfabrication process and then transported into an enclosed workspace for operation. This paper presents a new, 3D printing inspired method for in-situ fabrication of mobile magnetic microrobots with complex topology from a polymer filament on demand directly inside an enclosed operational environment. Through the use of a tip magnet on the filament, the target shape is formed by magnetic guidance from external electromagnetic coils which wirelessly project fields into the workspace as the filament is fed through a hot needle which is inserted into the workspace. A bending model and a shape planner are developed for predicting and controlling the fabrication process. Magnetically-active millimeter-scale robotic devices of different shapes and sizes are fabricated using polylactic acid (PLA) filament with diameter as small as 50 μm. As a demonstration of the in-situ formation of a functional microrobotic device, a force-sensing microrobot with integrated sensing spring is fabricated inside an enclosed space, and then is used to measure the manipulation force during a pushing experiment by optical deformation measurement. We thus show the utility of the fabrication method for creating complex microrobot shapes remotely in enclosed environments for advanced microrobotic applications, with the potential for scaled down applications in healthcare and microfluidics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".