Next‐Generation Embedded Printed Sensors for Near‐Field Monitoring of High‐Performance Composites
Bibliographic record
Abstract
Monitoring the structural health of composites during manufacturing and in‐service is desirable to alert against damage or deterioration of conditions beyond an acceptable level. Wireless sensors embedded into materials that can endure the forming and curing of carbon fiber‐reinforced polymer laminates will open the door to automated near‐field detection of key metrics such as temperature, strain, and manufacturing defects. Current sensing technologies are generally too intrusive and fragile to be reliably embedded into laminates or too expensive to be applied commercially. The development of embedded, low‐weight, small‐footprint sensors is reported here, and how these sensors can be used to monitor ply movement during the manufacturing process is demonstrated. These screen‐printed sensors consist of closed‐loop spiral coils excited externally with an AC magnetic field to generate a secondary field, which alerts on the change of relative position of each ply. This proof‐of‐concept work demonstrates how printed coil sensors can be fabricated to generate a high electromagnetic response, while minimizing their footprint in the laminate. It is determined that stacked silver coils, which are subsequently plated with copper to increase the conductance, are capable of producing signals that can be detected through over 3 mm of composite material.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".