Superstructured fiber-optic contact force sensor with minimal cosensitivity to temperature and axial strain
Bibliographic record
Abstract
In this work a new superstructured, in-fiber Bragg grating (FBG)-based, contact force sensor is presented that is based on birefringent D-shape optical fiber. The sensor superstructure comprises a polyimide sheath, a stress-concentrating feature, and an alignment feature that repeatably orients the sensor with respect to contact forces. A combination of plane elasticity and strain-optic models is used to predict sensor performance in terms of sensitivity to contact force and axial strain. Model predictions are validated through experimental calibration and indicate contact force, axial strain, and temperature sensitivities of 169.6 pm/(N/mm), 0.01 pm/με, and -1.12 pm/°C in terms of spectral separation. The sensor addresses challenges associated with contact force sensors that are based on FBGs in birefringent fiber, FBGs in conventional optical fiber, and tilted FBGs. Relative to other birefringent fiber sensors, the sensor has contact force sensitivity comparable to the highest sensitivity of commercially available birefringent fibers and, unlike other birefringent fiber sensors, is self-aligning with respect to contact forces. Unlike sensors based on Bragg gratings in conventional fiber and tilted Bragg gratings, the sensor has minimal cosensitivity to both axial strain and changes in temperature.
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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 | 0.001 |
| Open science | 0.001 | 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".