Fiber-mirror integrated compliant mechanical system for measuring force and displacement simultaneously
Bibliographic record
Abstract
Measuring force and displacement in some environments is not a straight forward task. For example, in the presence of flammable materials or when surgery robots are dealing with human body, force and displacement sensors should be redesigned for this use. In surgical applications, the sensors have to be electrically passive and EMI compatible. Many kinds of sensors have been introduced for this application, namely, peizoresistive, strain gauges, etc. Although these sensors have many advantages in this regard, they are neither compatible with EMI environments nor electrically passive. So, a novel method of optical sensing need to be developed for these applications. The optical sensors may be fabricated in micro scales to overcome aforementioned needs. Some examples of optical force sensors work based on light transmission in optical fibers [1]. The proposed optical sensor is quite simple and it measures both force and displacement using only one moving object. In the proposed system, light from an optical fiber is reflected by an integrated mirror fabricated through micromachining. The light that gets coupled back into the fiber is dependent on the gap and angular misalignment between the fiber and mirror that varies with applied force and displacement. Finite element modeling of the sensor was carried out with COMSOL and optical loss was estimated for different applied forces. This paper presents the design, modeling and performance behavior of the proposed system in terms of optical loss for different applied loads.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".