Factors governing burr formation during high-speed slot milling of wrought aluminum alloys
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Bibliographic record
Abstract
Burr formation and edge finishing are research topics with high relevance to industrial applications. To remove burrs, however, a secondary operation known as deburring is usually required. Deburring is more complex and costly when dealing with milled parts, because multiple burrs form at different locations with various sizes. Therefore, proper selection of process parameters to minimize the burr size is strongly recommended. Therefore, this requires an understanding of milling burr formation mechanism and the governing cutting parameters on milling burrs. In this article, a multilevel experimental study is arranged to investigate the effects of machining conditions, tooling and workpiece materials on burr size (height and thickness). Statistical tools are then used to determine the dominant cutting parameters on burr size and to effectively prescribe an operational window to control and minimize burr formation. It was found that optimum setting levels of process parameters to minimize each burr are different. The analysis of results shows the significant effects of cutting tool, feed per tooth and depth of cut on slot milling burrs.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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