Fatigue Performance of Type I Fibre Bragg Grating Strain Sensors
Bibliographic record
Abstract
Although fibre Bragg gratings (FBGs) offer obvious potential for use in high-density, high-strain sensing applications, the adoption of this technology in the historically conservative aerospace industry has been slow. There are several contributing factors, one of which is variability in the reported performance of these sensors in harsh and fatigue prone environments. This paper reports on a comparative evaluation of the fatigue performance of FBG sensors written according to the same specifications using three different grating manufacturing processes: sensors written in stripped and re-coated fibres, sensors written during the fibre draw process and sensors written through fibre coating. Fatigue cycling of the fibres is provided by a customized electro-dynamically actuated loading assembly designed to provide high frequency and amplitude loading. Pre- and post-fatigue microscopic analysis and high-resolution transmission and reflection spectra scanning are conducted to investigate the fatigue performance of FBGs, the failure regions of fibres as well as any fatigue-related effects on the spectral profiles. It was found that because of the unique fabrication method, the sensors written through the fibre coating, also known as trans-jacket FBGs, show better fatigue performance than stripped and re-coated FBGs with greater control possible to tailor the optical reflection properties compared to gratings written in the draw tower. This emerging method for inscription of Type I gratings opens up the potential for mass production of higher reflectivity, apodised sensors with dense or complex array architectures which can be adopted as sensors for harsh environments such as in defence and aerospace industries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".