Metallic contaminants in wood panel production process: Evaluating press plate damage and detecting potential using IR thermography and spectroscopy
Bibliographic record
Abstract
In the wood panel industry, metallic contaminants raise significant concerns, especially regarding the press plate's surface integrity, which requires a thorough inspection. This study investigated the effect of metallic contaminants on press plate damage and evaluated the use of infrared thermography (IRT) and infrared (IR) spectroscopy as non-destructive testing (NDT) methods for detecting these contaminants in wood panel manufacturing. Metallic contaminants embedded within lab-scale wood panels demonstrated their impact on the surface quality of both the press plate and the resulting panels. Moreover, confocal laser microscope analysis revealed that the surface roughness of the press plate surface was influenced by the specific alloy composition of contaminants, with steel and chromium contaminants exhibiting the more severe damage (e.g., mean roughness values of 59,80 and 84,64 μm, respectively). Thermography images exhibited the efficacy of IRT in detecting contaminants close to the surface of thin panels. However, an advanced camera is recommended for thicker panels and deeper contaminants to obtain a more accurate inspection. The Fourier-transform infrared spectroscopy (FTIR) evaluation revealed the presence of the metal-oxygen vibration band at approximately 668 cm −1 across all alloy compositions, suggesting its potential as a reliable reference for detecting metallic contaminants.
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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 | 0.000 |
| 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".