Effects of Knife Jointing and Wear on the Planed Surface Quality of Northern Red Oak Wood
Bibliographic record
Abstract
Jointing is a technique to obtain the same cutting circle for all knives mounted in a cutterhead of a peripheral knife planer. Initially the jointed land at the cutting edge has a 0 degree clearance angle that becomes negative with workpiece motion relative to the cutterhead and as the cutting edge wears. Jointed knives may crush cells on the planed surface and affect the quality and performance of wood for end uses. We evaluated the gluing properties of northern red oak planed surfaces that had been planed using one of three jointed land widths, over four levels of knife wear. Under these cutting conditions, surface roughness significantly influenced gluability more than cellular damage. In sum, gluing performance was positively affected by knife wear, and no variation in gluing performance among the jointed land widths studied existed. In samples after accelerated aging, the effects of wear on gluing were more pronounced, with an improvement in gluing performance, associated with an increase in surface roughness and permeability with increased knife wear. These results suggest a jointed land of 1.2 mm as the maximum allowable width for planing red oak wood prior to gluing. Also, the planed surface gluability of this wood may be enhanced using a knife with the rake face recession of 332 μm and the clearance face recession of 438 μm, which results in a surface roughness of 40 μm Rmax.
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.
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 | 0.000 |
| 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) | 0.001 | 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".