A Fiber Bragg Grating Sensing Structure for the Design, Simulation and Stress Strain Monitoring of Human Puncture Surgery
Bibliographic record
Abstract
In order to improve the precision and stability of puncture surgical operations to assist doctors in completing fine manipulation, a new of type puncturing needle sensor is proposed based on a fiber Bragg grating (FBG). Compared with the traditional puncture needle sensor, the new type of puncturing needle sensor is able to sense not only the axial force, but also the torque force during the puncture process. A spoke-type structure is designed near the needle tip. In order to eliminate the influence of temperature and realize temperature compensation, a reference fiber method using three FBGs is applied. FBG1 and the reference FBG2 are pasted on the upper and lower surfaces of the new-type elastic beam, and FBG3 is pasted into the groove on the surface of the new type of puncturing needle cylinder. The difference of Bragg wavelength between FBG1 and the reference FBG2 is calibrated with the torque force, while the difference between the Bragg wavelength of the FBG3 and the reference FBG2 is calibrated with the axial force. Through simulation and sensing tests, when the torque force calibration range is 10 mN·m, the torque average sensitivity is 22.8 pm/mN·m, and the determination coefficient R2 is 0.99992, with a hysteresis error YH and repetition error YR of 0.03%FS and 0.81%FS, respectively. When the axial force calibration rang is 5 N, the axial force average sensitivity is 0.089 nm/N, and the determination coefficient R2 is 0.9997, with hysteresis error YH and repetition error YR of 0.014%FS and 0.11%FS, respectively. The axial force resolution and torque resolution of the new type of puncturing needle sensor are 0.03 N and 0.8 mN·m, respectively. The experimental data and simulation analysis show that the proposed new type of puncturing needle sensor has good practicability and versatility.
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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".