Characterization of Spectral Response of a FBG Sensor Embedded in a Metallic Structure Using Laser Solid Freeform Fabrication
Bibliographic record
Abstract
This paper focuses on spectral response characterization of fiber Bragg grating (FBG) sensors embedded in metallic structures using laser solid freeform fabrication (LSFF). Fiber Bragg grating sensors are capable of measuring temperature, strain and pressure and can be embedded in metallic structures to monitor thermal and structural loads. Due to the sensitivity of the FBG sensors to high temperatures and stresses, the embedding process using LSFF is a challenging task. In the present work, a finite element model is developed to predict the stress and temperature fields adjacent to the fiber optic sensor embedded the metallic structure. The FE results are transferred to opto-mechanical model of the FBG sensor to predict the change in the spectral response of the sensor. Finally, the finite element model is verified using an existing analytical model which predicts the temperature field and melt pool geometry in LSFF process.
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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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".