Prediction of surface roughness of CO2 laser modified poplar wood via response surface methodology
Bibliographic record
Abstract
Due to the advantages of short treatment period, no wastewater and oil produced, the CO 2 laser is applied as an environment friendly thermal treatment for wood materials to improve the wood properties, such as appearance, color and wettability, among others.However, the morphological features of treated wood surface are also changed, which have negative effects on wooden product properties.To reveal the change tendency of surface roughness during laser irradiation, the common indexes of average roughness (Ra) and mean peakto-valley height (Rz) were chosen to evaluate the surface roughness.The response surface methodology was selected to arrange the experiments and analyze the influences of laser parameters on surface roughness.The results showed that the poplar wood got rougher with the increased laser power, but the surface roughness decreased with increased feed speed and path spacing, due to the total heat absorption varied under different combination of laser parameters.The ANOVA results showed that the selected quadratic models for Ra , Rz , Ra and Rz were significant due to the values of probability value ("Prob>F") less than 0,05.In this case, all the input laser parameters were also the significant model terms for variation of surface roughness.The values of correlation coefficient were very close to 1, which meant the selected quadratic models could give accurate prediction of surface roughness for laser treated wood.Therefore, it is of great significance to predict the surface roughness of the modified wood surface scientifically and to guide the selection of reasonable modification process parameters.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".