Investigation on Cutting Power of Wood–Plastic Composite Using Response Surface Methodology
Bibliographic record
Abstract
For the sake of improving the benefit of enterprise by reducing energy waste. RSM (response surface methodology) was used to investigated the cutting power of wood–plastic composite at different cutting conditions (rake angle, cutting speed, depth of cut, and flank wear). Based on the experimental results, a cutting power model with a high degree of fitting was developed, which can be used to predict cutting power and optimal cutting conditions. Meanwhile, the effects of rake angle, cutting speed, depth of cut, and flank wear and their interaction on the cutting power were probed by analysis of variance, and the significant terms were determined. Finally, the optimal cutting condition was obtained as follows: rake angle of 10°, cutting speed of 300 m/min, depth of cut of 1.5 mm, and flank wear of 0.1 mm. This parameter combination is suggested to be used for industrial manufacturing of wood–plastic composite in terms of the incredible machining efficiency and the lowest energy consumption.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".