Finite Element Modelling of the Effect of Adhesive Z-Connections on the Swelling of a Laminated Wood Composite
Bibliographic record
Abstract
This study used finite element analysis (FEA) to model the effects of adhesive Z-connections on the thickness swelling of laminated wood composites exposed to water. We hypothesized that the area density, diameter, and spatial distribution of adhesive Z-connections will influence the ability of Z-connections to restrain thickness swelling of the composites. We tested this hypothesis by modelling a wood composite in ANSYS FEA software v. 17.0 to explore the effect of moisture on the thickness swelling of the wood composite. The results were compared with those obtained experimentally. We then examined the effect of the area density, size (diam.), and spatial distribution of the adhesive Z-connections on the thickness swelling of wood composites. Our results showed a positive correlation between the number of adhesive Z-connections in the composites and restriction of thickness swelling following 72 h of simulated moisture diffusion. Similarly, increasing the size of adhesive Z-connections also restricted thickness swelling. In contrast, different spatial distributions of Z-connections had little effect on restraining thickness swelling. Our modelling approach opens up opportunities for more complex designs of adhesive Z-connections, and also to examine the effect of wood properties, such as permeability, density, and hygroscopic swelling ratios on the thickness swelling of laminated wood composites.
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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 | 0.001 | 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".