Application of finite element analysis in properties test of finger-jointed lumber
Bibliographic record
Abstract
Finger-jointed lumber production has now become the most extensively used method for spliced lumbers jointing together endwise. The properties of finger-jointed lumber are affected by many different factors such as the end- pressure. The main mechanical properties to be tested for structural use finger-jointed lumber include the modulus of elasticity in static bending and the bending strength. The most commonly used method for testing these properties at present is the experimental test. In this study, we used finite element method to investigate the end-pressure range, the modulus of elasticity in static bending and the bending strength for Pinus sylvistriv var. finger-jointed lumber under three different fitness ratios (0 mm, 0.1 mm, 0.3 mm). With finite element analysis (FEA) modelling results compared with the experimental test results, it is possible to find the relationship between these two kinds of results and use the FEA to predict the properties of finger-jointed lumber. The FEA applied in the end pressure tests showed a narrower range compared with the modelling results. It indicated that the FEA could be used in the prediction of the end pressure for finger-jointed lumber. The modelling results for modulus of elasticity (MOE) test and bending strength (MOR) test showed about 20% discrepancies compared with the experimental results. Moreover, the MOE modelling results showed the same trend as experimental results under three different fitness levels while the MOR modelling results showed the different trend. It can be concluded that the FEA is a feasible way in analysing the properties of finger-jointed lumber if the errors could be eliminated properly. Some modifications should be made in order to realize the prediction of the properties of finger-jointed lumber more accurately.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".