Triboelectrification in woodworking: influence of machining parameters on surface charges in planing, shaping, and sanding
Bibliographic record
Abstract
Triboelectrification occurs when two materials touch and separate, causing electrons to flow and generate electrical charges. In woodworking, this plays a crucial role, as the interaction between various tools and the wood itself can result in significant electric surface charges. This investigation explored the interaction between triboelectric activation occurring in woodworking processes like planing, shaping and sanding and processing parameters such as feed speed, rotation speed or cutting depth. The primary objective was to identify the specific processing conditions that most significantly contribute to triboelectric charge generation. Despite previous studies, there remains a lack of comprehensive understanding regarding the combined effects of machining parameters on triboelectric chargeing in different woodworking processes. Results showed that planers and shapers created negatively charged surfaces, while sanding led to positively charged surfaces. The charge varied with different machining parameters. Specifically, cutting depth had the most significant impact, followed by feed and cutting speed. The higher cutting depth and cutting speed reduced the surface charge on all machines, but increasing feed speed decreased the surface charge on planers and shapers while it increased after sanding. These insights provide a comprehensive understanding of the triboelectric phenomena, which is essential for optimising woodworking operations and mitigating electrostatic effects.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".