Measurement and interpretation of spatially registered bar-forces in LC refining
Bibliographic record
Abstract
Abstract A high resolution rotary encoder and a piezo electric force sensor are implemented in a 16-inch laboratory-scale low consistency refiner to explore the effect of plate gaps on bar-force profiles. The sensor replaces a short length of a stator bar and measures normal and shear forces applied during the passage of each rotor bar. The rotary encoder data is used to locate the rotor bars relative to the stator bar in which the sensor is located. Previous work with this type of force sensor focuses primarily on the distribution of the maximum force measured during the passage of each rotor bar over the sensor orbar passing event, BPE. In this work, force profiles for bar passing events are registered to the position of rotor bars relative to the stator bar in which the sensor is located. These registered force profiles are measured for a range of plate gaps and two different pulp furnishes. The angular reference provided by the encoder makes it possible to generate mean force profiles. As force data for individual BPEs is highly variable, these mean force profiles have potential to shed light on the fundamental mechanisms of mechanical refining. For large gaps, there is alate peakin the force profiles that occurs toward the end of the bar passing event. For gaps that are less than thecritical gap, below which fiber cutting occurs, there is anearly peakin the force profiles that occurs at the start of the bar passing event. It is hypothesized that the early peak represents thecorner forceand, therefore, that corner force is causal in the onset of fiber cutting. To explore this hypothesis, a model is presented connecting corner force and friction force to the progression geometric variables during the bar passing event such as the bar edge length engaged at any point in the bar passing event and the area covered by the rotor bar on the force sensor at any point in the bar passing event.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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 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".