The influence of cutting parameters on the surface quality of routed paper birch and surface roughness prediction modeling.
Why this work is in the frame
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Bibliographic record
Abstract
The objective of this study was to characterize the routing process to better understand the machining conditions that affect surface finish.Experiments were designed to determine the impact of cutting depth, feed speed, and grain orientation of the workpiece on the surface quality of paper birch wood.Statistical analysis showed that the cutting depth did not influence surface finish.Roughness depended greatly on feed speed and grain orientation, increasing linearly as the feed speed increased.The roughest surfaces were obtained by routing against the grain between 120 and 135 grain orientation, depending on the feed speed.Two models able to predict the surface finish based on initial cutting parameters were developed and compared.Both the statistical regression and neural network models were subjected to a validation procedure in which their performance was confirmed using data that were not used for the learning process.Results indicated that the neural network system estimates the surface roughness with less error than the statistical regression model.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| 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 it