Using the Ethaline Electropolishing Method on the Internal Surface of Additive Manufactured Tubes
Bibliographic record
Abstract
Electropolishing is a widely used technique for polishing additive manufactured (AM) components, while complex internal surface polishing remains a challenge. In this study, we explore the use of ethaline as an electrolyte and investigate the effects of temperature, time, stirring speed, and voltage on the electropolishing effectiveness for AM tubes without pre-treatment through orthogonal experiments. The optimal combination of these factors is then applied in further electropolishing experiments on straight tubes with large length-to-diameter ratios and an angled tube. Our results indicate that temperature has the most significant impact on internal surface electropolishing performance, and other factors' effects are also analyzed. Ethaline can be a promising electrolyte for internal surface electropolishing of AM components because of its high viscosity, which is validated by flow field simulation of the hydrodynamic conditions inside the tubes.
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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 | 0.001 | 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".