Burr edge occupancy: edge finishing index for milling machined parts
Bibliographic record
Abstract
Machining burrs are formed at all machined workpiece edges. One useful solution to decrease machining time and cost, in particular for milling parts, is to generate machined parts edges with minimum burr. This article proposes burr edge occupancy η s as an index to evaluate deburring difficulty and, consequently, adequate selection of suitable deburring methods. Initially the sensitivity of η s to cutting parameters must be evaluated. We investigated the main governing factors on η s when slot milling two types of aluminium alloys (from different families) that are used in the automotive and aerospace industries. The cutting parameters that led to edges with minimum η s are presented. It was found that, unlike most burr size attributes, η s is sensitive to variation of the cutting parameters used: cutting speed, family of material, and cutting tools. Lower η s means less time and effort for deburring and edge finishing of machined parts. Furthermore, η s measurement is more convenient than the procedures used to measure other burr size attributes, including burr height (b h ) and burr thickness (b t ).
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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 | 0.000 | 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".