Effect of Tool Tip Radius on ring Debarker Performance
Bibliographic record
Abstract
The effects of the tool tip radius on debarking quality of unfrozen and frozen black spruce logs were studied. The power, energy consumption and torque on frozen conditions were also studied. A proto-type one-arm ring debarker was used. The experiment consisted of debarking logs using three tool tip radii (40, 180, and 300 µm) for each temperature (-20°C and +20°C). The rotational and feed speeds, tip overlap, and rake angle were kept constants. Debarking quality was evaluated by two criteria: the proportion of bark remaining on log surfaces and the amount of wood in bark residues (WIB). Log characteristics, used as covariates, ie dimensions, eccentricity, bark thickness, knot features, bark/wood shear strength (BWSS), basic densities and moisture contents of sapwood and bark were measured, as well as total removed material after debarking. The results showed that tool tip radius had a significant effect on debarking quality of frozen and unfrozen logs. The proportion of bark on log surfaces increased and the amount of WIB decreased as tip radius increased. At the same applied radial force, a wider tip radius showed a shallower tip penetration leaving bigger regions of bark on the log surfaces. In contrast, a narrower tip radius showed a deeper tip penetration resulting in important wood fiber tear-out. The bark thickness and inner bark MC also affected debarking quality. The mean power, mean torque, and energy consumption increased as the tip radius decreased. However, this effect will depend on the choice of the applied radial force during debarking. Motor performance was also affected by the total removed material, log diameter, and BWSS. Overall, the results highlight the importance of choosing an adequate combination of tool tip radius and applied radial force to obtain the most profitable debarking quality with an efficient energy consumption.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".