Numerical modeling for embedding of fiber Bragg grating sensors within metallic structures using laser solid freeform fabrication
Bibliographic record
Abstract
This paper aims at numerical modeling of laser solid freeform fabrication (LSFF) process utilized in embedding of fiber Bragg grating (FBG) sensors inside metallic structures. This model is used in characterization of the process. Fiber Bragg grating sensors have the capability of being embedded inside structures for monitoring temperature, strain and pressure. 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 paper, a finite element model is developed to predict the stress and temperature fields adjacent to the fiber optic sensor inside the metallic structure and maps them to the spectral response of the sensor. Using this method, the stress-strain and temperature conditions of the sensor during the embedding process can be monitored and the modeling data can be used for process control and characterization to minimize the effects of high temperatures and residual stresses having negative effects on sensor coherence with the surrounding media. Finally, the proposed model is validated with an existing analytical model predicting temperature field and melt pool geometry.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".