Show HN: I made a new sensor out of 3D printer filament for my PhD
A PhD project using 3D-printer filament and optical fibers to build a low-cost bend-localizing sensor is drawing attention for its simplicity and potential in soft robotics, wearable controllers, and surgical instruments. Commenters compare it to established fiber-optic sensing methods like OTDR and fiber Bragg gratings, noting that while the core idea is conceptually related, this approach trades precision for easy fabrication and integration into “squishy” systems. The work is widely praised for clear communication, practical design choices, and perceived commercial promise, with some side debate over academic norms such as PhD duration and publication expectations.
Overall reception
- Thread is overwhelmingly positive; many call the sensor clever, beautiful, and “what hacking is all about.”
- People appreciate that it uses cheap, accessible materials instead of specialized lab-only processes.
- Several praise the write-up as unusually readable for PhD work.
Clarity and communication
- Early readers say the article initially buried the answer to “what does it do?” (bend localization).
- After the post was updated with a simple “bent rope / squishy cable that knows where it’s bent” explanation, readers report it’s much clearer and more trustworthy.
- Suggestions include: give concrete use cases early, add a short video of the core effect, and avoid rainbow/jet colormaps for accessibility.
Potential applications
- Soft robotics dominates: flapping wings, swimming fins, compliant arms, soft robot “legs” and skins.
- Other ideas: data gloves, robotic proprioception, surgical and medical shape sensing, industrial monitoring, tactile skins using 2D/3D layouts, and golf swing tracking (though practicality there is debated).
- Some imagine long runs along robot arms, with learned mapping from sensor data to end-effector pose.
Relation to existing technologies
- Multiple commenters compare it to time-domain reflectometry / OTDR, distributed acoustic/temperature sensing, fiber Bragg gratings, and early data gloves / Power Glove.
- Consensus: conceptually similar in “shape sensing,” but this design trades high-speed timing and expensive optics for simple, coded air gaps and multiple fibers, optimized for soft, low-cost systems.
- Cost and complexity of FBG-based systems are highlighted as motivation for this simpler approach.
Technical questions and ideas
- Questions about multiple bends and directionality: some worry attenuation will just add; others note that relative attenuation patterns across fibers and Bayesian modeling could disambiguate, up to saturation.
- Ideas: denser gaps to reduce dead zones, multi-lobe (e.g., 3-way) layouts for bend direction, different refractive media in gaps, space-filling curves for 2D/3D tactile sensing.
- Fabrication discussion covers using TPU vs PMMA, why total air gaps outperform scratches or partial cladding removal, and limitations of 3D printing due to optical losses.
- Kalman filtering is currently simple 1D smoothing, mainly chosen for familiarity and potential future sensor fusion.
IP and PhD context
- Commenters encourage commercialization and IP protection; the university is reportedly pursuing this.
- Side discussion analyzes how a 3-year PhD was possible, the role of prior master’s degrees, and structural differences between course-heavy and publication-heavy PhD programs.