Numerical Prediction of Engineered Wood Flooring Deformation
Bibliographic record
Abstract
Dimensional stability is of primary importance in the use of layered wood composites such as engineered wood flooring.It is largely due to the physical and mechanical properties and moisture content changes of each layer.Therefore, the non-homogeneous adsorption or desorption of moisture by the composite may induce its deformation, thus decreasing product value.The objective of this study was to develop a finite element model of the hygromechanical cupping in layered wood composite flooring.The model is based on two sets of equations: 1) the three-dimensional equations of unsteady-state moisture diffusion, and 2) the three-dimensional equations of elasticity including the orthotropic Hooke's law, which takes into account the shrinkage and swelling of each layer.The proposed model was used to predict the deformation of an engineered wood flooring strip following desorption by the top surface.The model was solved by the finite element method, and the calculated cupping was validated against experimental data.The results show that the proposed model can be successfully used to simulate the non-homogeneous moisture movement and the resulting cupping deformation in layered wood composites such as engineered wood flooring strips.For both predicted and measured deformation, roughly 80% of the cupping deformation appears after 3 days of conditioning.The low water vapor diffusion coefficient of the urea-formaldehyde film used between the surface and core layers of the strip plays a key role in the deformation process.After 42 days of conditioning, the model results overestimated the experimental results by 12% but were within one standard deviation of the experimental results.The model presented in this study appears to be a useful tool for product design purposes.
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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 | 0.001 | 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".