Smart Roller: Soft Sensor Array for Automated Fiber Placement
Bibliographic record
Abstract
Abstract Rollers and wheels are widely used in industry and transportation, but there is seldom direct information about contact forces. A smart roller is introduced which provides real‐time pressure measurements from a soft, elastomer‐coated cylinder. The roller is designed for automated fiber placement (AFP) machines, which are widely used in the aerospace industry to manufacture complex composite parts. For optimum process performance, real‐time feedback is highly desirable for detecting flaws during manufacturing. The sensor replaces the elastomer outer layer of a standard roller with 4 by 13 tactile pixels (taxels) of soft capacitive sensors, which provide more than 1 pF of change in capacitance per taxel over a pressure range of 1 MPa. Sensors are made of silicone and mounted on a flexible printed circuit board on which a microcontroller with Bluetooth‐Low‐Energy collects and transmits capacitance data. The sensor dielectric layer is composed of pillars that increase layer compliance and sensitivity while also providing the stiffness of typical industrial rollers. The ability of the roller to measure real‐time local compaction pressure at typical manufacturing speeds enables the monitoring of spatially‐resolved pressure‐time curves, which can be used to predict and control adhesion.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".