Design of an Embedded Sensor Network for Manufacturing Process Monitoring, Quality Control Management and Structural Health Assessment of Advanced Composite Structures
Bibliographic record
Abstract
This paper proposes the use of an embedded network of fiber optic sensors for process and Structural Health Monitoring (SHM) of Resin Transfer Molded (RTM) composite structures. A single sensor network is used at each stage of life of a RTM composite panel: flow monitoring, cure monitoring and health monitoring. A laboratory scale RTM apparatus was designed and built with the capability of visually monitoring the resin filling process. A technique for embedding fiber optic sensors into the mold has also been developed. Both Fiber Bragg Gratings (FBG) and Etched Fiber Sensors (EFS) have been embedded in composite panels using the apparatus. Etched Fiber Sensors have the capability of detecting the presence of resin. The sensors have proven to be capable of detecting the presence of resin at various locations as it is injected into the mold and have the capability of being multiplexed with FBGs thus reducing the number of ingress/egress locations required per sensor. Two FBGs and three EFSs were embedded on a single optical fiber. Tensile test specimens that contain embedded FBG sensors have also been produced with this apparatus. These specimen and embedded sensors have been characterized using a strain gage and a material testing machine. FBG sensors have been embedded into composite panels also in a manner that is conducive to detecting Lamb waves generated with a centrally located PZT. To detect Lamb waves a high speed, high precision sensing technique is required for embedded FBGs, since these guided waves travel through the material at very high velocities, presenting relatively small strain amplitudes. A technique based in a filter consisting of a second FBG was developed. Since this filter is not dependant on moving parts, it does not limit the velocity or frequency at which the tests can be performed. Preliminary tests performed using this filter showed that it is possible to detect Lamb waves with amplitudes smaller than 1 microstrain. A damage detection algorithm has been developed and is applied to this system in an attempt to detect and localize damages (cracks and delaminations) in the composite structure.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".