A Simple Approach for a 3D Printed Single-Strand Based Compliant Deflection Sensor Input Device
Bibliographic record
Abstract
ABSTRACT Recent advances in additive manufacturing (AM) have transformed the manufacturing of polymers, enabling the production of highly customized parts and components with enhanced mechanical, electrical, and thermal properties. These properties are of great use in 3D printed compliant structures. The emergence of electrically conducting filaments for fused deposition modeling (FDM) 3D printing opens up the possibility of integration of printed circuits and electrical functionalities directly into AM components. The combination of compliant mechanisms and embedded sensors through the means of AM provide flexibility in the design and integration of sensors into structures, where traditional sensors might not be applicable or possible to be mounted using traditional manufacturing methods. In this work, the focus is on multi-material 3D-printed components made out of PLA and conductive filament. The simplest form of an electric circuit and compliant structure were chosen to gather data on electrical properties in relation to the elastic deformation of a cantilever or bending beam with an embedded single-strand of conductive filament. The commercially available Electrically Conductive PLA (ProtoPlant Inc., Vancouver, USA) and standard PLA filament were used on the Creator Pro 2 (Flashforge 3D Technology Co., Ltd, Zhejiang, China) printer. A single single-strand conductive cantilever (SSCC) has the dimensions 40 mm x 1 mm x 0.4 mm in length, width and depth and is manufactured out of conductive and non-conductive, regular PLA filament. Experiments to determine volume related electrical resistance in relation to width and thickness of printed circuit strands were conducted, as well as electrical properties of a single-strand conductive cantilever in relation to the elapsed time after deflection were measured. A non-time-constant drifting behavior of the electrical resistance was observed after deflection and after return to the initial state. As implementation and proof of concept, a 2 DOF input joystick with two sequential but perpendicular single-strand filament bending beams was manufactured and electrical resistance was measured to display the real time deflection direction and amplitude as input on a 2D grid.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".