Machinability of Luxury Vinyl Tiles during Plain Milling Using a Helical Cutter
Bibliographic record
Abstract
In order to better provide a theoretical basis for the machining of luxury vinyl tiles, a helical milling experiment was conducted by using diamond cutting tools, and special attention was given to the trends of cutting force and surface roughness in respect to tool geometry and cutting parameters. The results showed that the resultant force was negatively correlated to the helix angle and cutting speed, but positively correlated with the cutting depth. Then, that the surface roughness increased with a decrease of the helix angle and an increase of cutting depth, while as cutting speed raised, the surface roughness first declined and then increased. Thirdly, the cutting depth was shown to have the greatest influence on both cutting force and surface roughness, followed by helix angle and cutting speed. Fourth, the contribution of cutting depth only was significant to cutting force, while both the helix angle and cutting speed had insignificant influence on the cutting force and surface roughness. Finally, the optimal cutting conditions were proposed for industrial production, in which the helix angle, cutting speed and cutting depth were 70°, 2200 m/min and 0.5 mm, respectively.
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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) | 0.001 | 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".