Characterizing macro-voids of uncompressed mats and finished particleboard panels using response surface methodology and X-ray CT
Bibliographic record
Abstract
Abstract Macro-voids in the core of uncompressed particle mats and pressed particleboard manufactured from novel particleboard furnishes were characterized using a response surface method with mixture design and X-ray CT technology. Industrial particles were screened into core-fine, medium, and coarse size classes and their dimensions were measured. Wooden blocks measuring 10 times the mean dimensions of these particles were cut and used as surrogates for the industrial particles. Novel particle mixtures were prepared by mixing together various proportions from each particle size class. The mixtures were packed to simulate particleboard mat formation and a pre-pressed particle mat. Panels were then fabricated from the industrial furnish mixtures. The void fraction of the packed particles and the finished panels were measured and correlated with the IB strength and edge screw withdrawal resistance. Results indicated that densely packed hammer-milled industrial particles had a maximum void fraction of 63.2%. The void fraction of a randomly packed, dense particle mat was described using a full cubic model. In both particle mats without resin and the pressed panels, increasing the core-fine content decreased void volume, whereas increasing coarse particle fraction increased void volume in the mat only. The macro-void ratio in the pressed panels increased exponentially with void fraction for the randomly packed, loose particle mats. Particle mixtures that resulted in boards with the smallest void fraction were not necessarily the strongest boards; low density particleboard panels made from the novel 100% coarse mixture were found to have the highest mechanical properties.
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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 | 0.000 | 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".