Effects of helix angle and feed per knife on the surface quality of sugar maple and black spruce boards produced by planing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 it