Multi-Material 3D Printing of Highly Sensitive Flexible Multi-Layered Tactile Sensors
Bibliographic record
Abstract
Additive manufacturing technologies like fused deposition modelling FDM 3D printing have become popular in academic research for their affordability and versatility.This paper presents a method for creating soft, multi-layered tactile pressure sensors with high sensitivity and a wide sensing range using FDM 3D multi-material printing.Combining conductive carbon black thermoplastic polyurethane (CBTPU), as the sensing material and polyvinyl alcohol (PVA), as the supporting material, allowed the fabrication of novel pressure sensors with enhanced mechanical compressibility and a wide electromechanical sensing range.For comparison, a solid sample of the same conductive material was fabricated and tested.The 3D-printed multi-layered model increased the sensor's compressibility by more than 6-fold compared to the solid sensor.This enhancement results in a greater change in electrical resistance by 9-fold.The multi-layered sensor showed repeatable behaviour in response to cyclic pressure suggesting their great potential for use in wearable electronics and robotic applications.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".