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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".