Development of Multi-degrees of Freedom Optical Table Dynamometer
Bibliographic record
Abstract
Accurate measurements of cutting forces are critical in machining operations for maximizing production, detecting tool wear and failure, adaptive control and monitoring. Traditionally, cutting forces are measured using piezoelectric quartz force sensors or strain gauges. These types of sensors have limitations in that they are unable to measure static force signals. The quartz crystals of a piezoelectric force sensor generate an electric charge only when force is applied to or removed from them. Strain gauges suffer drifts over a long period time. In order to overcome the challenges, a novel table dynamometer is developed based upon laser optics to measure planar forces and moments in both the static and dynamic range. The developed table dynamometer allows the measurement of both the in plane linear movements and the in-plane rotations. In order to achieve high sensitivity, a monolithic, flexure-based mechanical amplifier is adopted into the proposed table dynamometer. A prototype of the developed system is fabricated and the sensitivity and frequency bandwidth of the system are experimentally investigated. The results showed good agreement between the optical force sensor and a reference force transducer. The proposed dynamometer is tested for use in the measurement of cutting forces and compared with a conventional piezoelectric dynamometer.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".