Effects of helix angle and feed per knife on the surface quality of sugar maple and black spruce boards produced by planing
Bibliographic record
Abstract
A conventional straight knife cutterhead and three helical knife cutterheads were tested for planing sugar maple (Acer saccharum Marsh.) and black spruce (Picea mariana (Mill.) B.S.P.) woods. The effects of helix angle and feed per knife (FK) on roughness, tactical perception, and anatomical features of the planed surfaces were evaluated. Rk and Rpk parameters were found to be more descriptive in evaluating the roughness of these woods and proved to be good indicators of tactile perception. Roughness increased as the helix angle and feed per knife increased for both wood species. Sugar maple showed smoother surface than black spruce. Surfaces planed with helical knives showed a fuzzy texture resulting from cell-wall fibrillation. For sugar maple wood, the differences in roughness between straight and 40° helical knives were small. Therefore, the latter must be preferred for planing this species. Roughness of planed black spruce also increased as helix angle increased and in this case its effect depended on FK. The straight knife produced the lowest roughness, for all studied FKs. Planing with helical knives produced higher roughness as a result of cell-wall fibrillation. This defect was more pronounced than that of sugar maple, and by far more present with helical knives of 50° and 60°. Therefore, straight knives working at low FK (1.3 mm) should be preferred for planing black spruce wood when roughness is a critical concern. Possible benefits provided by rougher surfaces planed by helical knives are discussed.
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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) | 0.001 | 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) | 0.001 | 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".