Integration Methods of Sensors in FRP Components
Bibliographic record
Abstract
Structural health monitoring is an important research topic in the field of fiber reinforced plastics (FRP). An effective way to detect defects or overloads in these FRP has still not been found. One way to monitor the actual state of FRP components is via integrated sensors. Integrating current standard sensors negatively affects the flux of force. Therefore investigations about integration methods of sensors in FRP components have been made. The integration of an optical fiber sensor into FRP profiles via a pultrusion process was investigated. It could be shown that the pultrusion process is suitable method for the integration of fiber optic sensors for strain measurements. Another investigated sensor principle was the integration of piezoelectric polyvinylidene fluoride (PVDF) fibers via a vacuum assisted process. The PVDF fibers were integrated into 3-point bending specimen and the piezoelectric effect was tested with and without polarization. The investigation showed that it is possible to measure the piezoelectric effect of PVDF fibers integrated into a 3-point bending test specimen. It could also be shown that carbon fibers can be used as textile electrodes for the measurement of the generated charge on the PVDF surface.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".