Analysis of Al7136 surface roughness in end milling process based on discriminant analysis
Bibliographic record
Abstract
Abstract. The objective of this research is to determine how cutting parameters influence the transversal surface roughness of Al7136 aluminum alloy when subjected to end milling. To achieve this, 150 experiments were performed, systematically varying the cutting speed (v), depth of cut (ap), and feed per tooth (fz). The method used for analysis is discriminant analysis, which generated three discriminant functions. The results indicate a significant correlation between these variables and surface roughness. The discriminant functions provided an accurate classification of observations into different levels of roughness, and the coefficients of these functions showed the relative importance of each independent variable in discriminating between different levels of roughness. Covariance and correlation analyses were performed to further understand the interactions between the independent variables within each experimental group. The conclusions suggest that adjusting cutting parameters can be performed to achieve a reduction in the roughness of the machined surface, contributing to the improvement of product quality in end milling of the Al7136 alloy.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".