3D Structural Electronics Via Multi‐Directional Robot 3D Printing
Bibliographic record
Abstract
Abstract Multi‐material 3D printing is an innovative technology that sparks a revolution in combined structural 3D printing with different functional materials. However, current multi‐material printing systems work alone and cannot take full advantage of each printing, and the limited freedom of existing 3D printers confines diversity of structural designs. Here, a double‐arm robotic 3D printing system combined with paste‐based direct ink writing (DIW) and filament‐based fused filament fabrication (FFF) is reported as a new fabrication solution for customized 3D structural electronics. Collaboration between two robot arms brings about a spontaneous sintering effect of metal nano‐particles (minimum resistance = 14.5 Ω per 10 mm) by addition of the following extruded layer through FFF for the fabrication of embedded sensors, which reduces the post‐processing steps and saves energy for sintering compared with the conventional parallel multi‐material 3D printing processes. The extra freedom of motions from two robot‐arm 3D printing enables multi‐directional curvilinear printing for 3D structural electronics, such as a double‐helix‐shaped embedded sensor. In addition, precise robot motion enables the electrical registration and vertical integration of multi‐layered printed circuit boards. Establishment of new double‐robot 3D printing system expands design freedom and realization of 3D structural electronics.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".