Fatigue Performance of Type I and Type II Fibre Bragg Gratings Fabricated by Femtosecond Laser Inscription through the Coating
Bibliographic record
Abstract
Strain sensing technology using fibre Bragg grating (FBG) sensors is an attractive capability for aerospace structural health monitoring (SHM) and assessment because they offer resistance to harsh environments, low maintenance, and potential for high density and high strain sensing. The development of FBG inscription techniques through the fibre polymer coating using infrared (IR) lasers has overcome the mechanical weaknesses introduced by removal of the fibre coating, which is typically required for conventional UV laser inscription of FBGs. Type I and Type II femtosecond gratings are fabricated using through-coating inscription techniques, but the higher laser energy used for Type II gratings damages the glass fibre core, impacting mechanical performance. This paper investigates the fatigue performance of Type I and Type II through-coating FBG sensors with different fibre geometries and photosensitisation approaches to evaluate their overall reliability and durability, with a view to assess their performance for potential use in civil and defence SHM applications. The fatigue performance of FBG sensors was assessed under high-strain and high-frequency mechanical loading conditions by using a custom-designed electro-dynamically actuated loading assembly. In addition, pre- and post-fatigue microscopic analyses and high-resolution reflection spectrum characterisation were conducted to investigate the failure regions of the fibres and the effect of fatigue loading on reflection spectrum features. As expected, Type I gratings had a significantly higher fatigue life compared to Type II gratings. However, Type II gratings performed significantly better than conventional UV laser-inscribed FBGs and electrical foil strain gauges. Type II gratings withstand higher temperatures, and are therefore more suitable for application in harsh environments.
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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".