3Duino: A Low-Barrier Platform for Prototyping Interactive 3D-Printed Devices.
Bibliographic record
Abstract
We present 3Duino, a unified software and hardware platform that enables users to prototype interactive devices without specialized expertise in mechanical design, electronics and programming. With 3Duino, users can assign desired input and output functionalities (e.g., touch input, motion sensing, lighting, or physical actuation) directly to a 3D model. For each specified function, 3Duino automatically generates the necessary internal interactive structures, designed for single-piece 3D printing, minimizing post-processing and ensuring seamless compatibility with the 3Duino hardware. In addition, 3Duino also allows users to define interaction logic using natural language statements through its interface. Based on these statements, the system generates the corresponding control code to run on the hardware. To inform the design of 3Duino, we conducted a formative study to identify key challenges in existing workflows. We then developed and evaluated 3Duino through a user study with 12 participants, which showed that the platform lowers the barrier to prototyping interactive 3D-printed devices, enabling users to create functional, interactive artifacts with ease.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 0.007 |
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".