Particleboard Surface-Roughness Classification System Modeling, Simulation, and Bench Testing
Bibliographic record
Abstract
Particleboard panels are widely utilized as a raw material in the wood processing industry. It ends up as furniture, cabinets, and other industrial products. One of the problems particleboard mills face concerns the surface quality of their boards. As the demands of customers become more precise, very thin overlays are becoming more popular. Thus the problem of surface quality control and classification is clearly identified. In this paper, a particleboard surface-roughness classification system is modeled, simulated, and implemented. The particleboard model is based on the characterization of surface anomalies (pinholes, sander streaks, and grooves). Furthermore, an optical stylus surface-roughness measurement system is also modeled in order to determine whether it can be used to characterize a particleboard “on-ine.” A classification algorithm is proposed to serve as an aid to the quality control operator. Simulation results are presented illustrating the change of surface roughness with increasing amounts of surface anomalies. A classification algorithm is used to sort the simulated panels into different classes. A trial bench test using 225 panels is made to determine the applicability of this system to the industrial context.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".