Determination of physical and mechanical properties of finishing papers used for wood-based composite products.
Bibliographic record
Abstract
There has been a noticeable trend in the furniture and flooring industries in using finishing products (decorative paper, foil, wood veneer, and so on) of different quality on both surfaces of raw engineered wood-based panels.Under variable temperature and RH conditions, this practice can result in dimensional instability.The objective of this study was to determine the key properties of five finishing papers affecting the hygromechanical behavior of wood-based composite panels.The diffusion coefficients, swelling properties, and tensile modulus of elasticity (MOE) of the finishing papers were determined.The results show that the finishing papers studied are anisotropic in terms of their physicomechanical properties.For papers impregnated with melamine-formaldehyde resin, the tensile MOE decreases with an increase in resin content.Swelling is the most significant dimensional change.The range of variation of the linear expansion coefficients is between 0.03 and 0.17 in the fiber direction and between 0.08 and 0.28 in the transverse direction for raw papers.The linear contraction coefficients vary between 0.05 and 0.31 in the fiber direction and between 0.07 and 0.28 in the transverse direction.The behavior is different during adsorption and desorption.Effective diffusion coefficients of the papers tested vary between 4.5 Â 10 À12 and 8 Â 10 À11 m 2 s -1 .
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".