Multiplexed FBG and etched fiber sensors for process and health monitoring of 2-&3-D RTM components
Bibliographic record
Abstract
This paper presents research being conducted on the use of a combination of fiber optic sensors for process and health monitoring of resin transfer molded (RTM) composite structures. A laboratory scale RTM apparatus has been designed and built with the capability of visually monitoring the resin filling process and embedding fiber optic sensors into the composite. Fiber Bragg gratings (FBG) and etched fiber sensors (EFS) have been multiplexed and embedded in quasi-2-D panels and 3-D hollow semicircular structures using a novel ingress/egress technique for the purpose of both process and structural health monitoring. The objective of this work is to demonstrate the simultaneous usage of FBGs and etched fiber sensors on a single optical fiber for resin flow monitoring and strain monitoring throughout the life of the composite. Three specimens are presented: one quasi-2D panel with two FBGs and three etched fiber sensors, one semicircular tube with two etched fiber sensors and one semicircular tube with two FBGs. Etched fiber sensors have been correlated with visual inspection to detect the presence of resin. Specimens with embedded FBG sensors have been tested in a tensile test machine to characterize the FBGs for strain monitoring.
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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 | 0.001 | 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) | 0.001 | 0.000 |
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".