EFFECT OF CUTTING PARAMETERS ON DUST EMISSION AND SURFACE ROUGHNESS DURING HELICAL PLANING RED OAK WOOD
Bibliographic record
Abstract
Cutting parameters can influence the chip thickness and resulting dust emission and surface quality during wood machining. The purpose of this study was to evaluate the effect of cutting parameters on the airborne dust emission (0.1-15 μm) and surface roughness produced by helical planing red oak wood. Two cutting depths (0.5 and 1.0 mm) and eight feed speeds (from 6 to 20 m/min) were combined to obtain four average chip thicknesses (from 0.10 to 0.22 mm). The results showed that dust emission decreases as average chip thickness increases. Dust emission also increased for the higher cutting depth but for thinner chips (0.10 mm thick). For thicker chips, dust emitted was similar for the two studied cutting depths. Regression models for estimating dust emission for each particle size fraction as a function of average chip thickness and cutting depth were developed. Furthermore, higher values of average chip thickness produced higher surface roughness. The best helical planing condition was obtained when using 0.5-mm cutting depth, 0.18-mm average chip thickness, and 16 m/min feed speed. This condition was the fastest feed speed allowable to obtain the best surface quality while minimizing dust production.
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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 | 0.000 | 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 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".