Dynamic calibration of tri-axial piezoelectric force transducers
Bibliographic record
Abstract
Applied dynamic loads are often difficult to measure accurately due to the dynamic response of the sensor used and the dependence of the sensor's sensitivity on the mounting and loading details. For tri-axial force transducers, which are capable of measuring forces along the axial direction and along both directions of the transducer's face, dynamic calibration is further complicated by the coupling of the sensor's measurement directions. For this reason, a new apparatus for dynamic calibration of normal and tangential directions of a tri-axial piezoelectric force transducer has been constructed and tested. The calibration force is provided from a spring loaded uni-axial impulse hammer. The apparatus allows for calibration at a variety of calibration angles and speeds; the loading for all cases of a nonzero calibration angle is oblique, with the point of force application being eccentric to the centerline of the force transducer's normal axis. As such, tangential loads are always accompanied by a normal load. The calibration results show that the normal direction correction factors have a systematic dependence on the calibration angle; the tangential correction factors show some scatter but do not appear to be dependent on the calibration angle.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".