Investigation of Magnetic Abrasive Finishing for Internal Polishing of Aluminium Tubes Using DOE
Bibliographic record
Abstract
Magnetic Abrasive Finishing (MAF) is one of the nontraditional machining methods with vast applications in high-tech industries such as medical, aerospace and semiconductor manufacturing areas. Several researches have been done in order to studying the influence of various parameters on magnetic abrasive finishing process and characteristics of finished surface. The present paper investigates the effects of some effective parameters such as mesh size of the abrasives, the weight of the abrasive powder and the number of cycles on internal surface roughness of Aluminum tube. The optimum percentage of oil should be added to the abrasive powder was attained and applied for the main tests. Then, design of experiments (DOE) methods and Analysis of Variance (ANOVA) have been applied to determine significant factors and also to obtain an equation based on data regression.
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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 | 0.001 |
| 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 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".