Cutting Force and Surface Roughness during Straight-Tooth Milling of Walnut Wood
Bibliographic record
Abstract
Walnut (Juglans regia L.) is widely used in wood furnishings, and machinability is a key factor for improving product quality and enterprise benefits. This work focused on the influence of the rake angle, depth of cut, and cutting speed on the cutting force and machined surface roughness during the straight-tooth milling of walnut. On the basis of the experimental findings, a mathematical model was created using a response surface methodology to determine the relationship between the cutting force and the cutting conditions, as well as the relationship between the surface roughness and the cutting conditions. Variance analysis was used to study the significant contributions of the interactions of various factors and two-level interactions to the cutting force and surface roughness. The optimized combination of milling conditions, resulting in lowest cutting force and surface roughness, was determined to be a rake angle of 5°, a depth of cut of 0.6 mm, and a cutting speed of 45 m/s.
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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 | 0.000 | 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 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".