Implementation of a mechanics based system for estimating the strength of a board using mixed signals of MOE and x-ray images
Bibliographic record
Abstract
The most accurate way of identifying the strength of lumber requires destructive testing which is clearly not useful for production of lumber. An intelligent mechanics-based lumber grading system was developed to provide a better estimation of the strength of a board nondestructively. In this study a mechanics-based system was implemented to estimate the strength of a board, using only one combined feature extracted from MOE (modulus of elasticity) profiles and x-ray images. The x-ray image analysis involved extracting the useful parts of the image and compensating for the effect of vibration. After that, the image was passed through a directional low-pass filter to reduce the noise. Furthermore, the image was resized by interpolation in such a way that the size of the signal was the same as the real size of the board, which is 89[mm] 4900 [mm]. The image was passed through a threshold filter to separate the knots based on the fact that the denser knots produce "high hills" in the x-ray image. Finally, information on all the knots such as geometry and location were detected from the threshold image. The knot size and location were fed to an FEM processor to generate the physical model and the associated stress field. In this study, simulating grain direction by analogy to fluid flow and reorienting the element coordinate system along the flow line direction generated the slope of grain. The stress fields were then fed to a feature-extracting-processor which produced one strength predicting feature. A coefficient of determination of 0.4158 was reached using x-ray images alone. The MOE part of the system uses output of CLT machine which contains top and bottom profiles. Due to lumber curvature, one profile may be higher than the other one. By averaging the two profiles this effect will be compensated. Since the grip length for tension tests was 15% of beginning part and end part of each profile, these parts were discarded. The minimum value of the remaining part was the base for calculating the strength. A coefficient of determination of 0.5805 was achieved using MOE alone. Then, the two MOE and x-ray extracted features were combined to a single feature to estimate the strength of the boards. By applying the described algorithm to a database of more than 1000 boards to estimate the strength, a coefficient of determination of 0.6417 was achieved. The results show a way to improve the accuracy of lumber grading systems using combined signals.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".