Development of a piezoelectric force sensor for a chip refiner
Bibliographic record
Abstract
Chip refiners are used to separate individual fibres from the wood matrix through the application of cyclic compressive and shear forces. The work presented here deals with the development of a two-axis piezoelectric force sensor to measure these cyclic forces in directions normal and tangential to the motion of refiner bars. The sensor consists of a small probe tip that replaces a portion of a refiner bar and is supported on four piezoelectric elements inside a housing. Stresses applied to pulp and wood material, at the surface of the probe, are thus transmitted to the piezoelectric elements, which respond by producing voltage signals. Signals from two of the four piezoelectric elements are used to determine forces in the normal and tangential directions during refining. A prototype sensor was tested in an atmospheric-discharge laboratory refiner. Impacts from individual bar crossings could clearly be discerned even at the maximum operating speed of the refiner. At low refiner speed, detailed measurements of the magnitude of the normal and tangential forces throughout a bar crossing were obtained. However, resonant vibrations of the sensor made it difficult to obtain such information when running the refiner at maximum speed. A number of design modifications are discussed, with the aim of improving the sensor performance for applications in larger-scale commercial refiners.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".