Review—Electropolishing of Additive Manufactured Metal Parts
Bibliographic record
Abstract
Most metal AM technologies are rapidly approaching, and in some cases even exceeding the Technology Readiness Level 8, indicating that they are widely available and capable of completing a wide range of projects despite identified process restrictions. Thanks to significant technological progress made in the last decade, more industries are incorporating metal additive manufacturing in their production process to obtain highly customized parts with complex geometries. However, the poor surface finish of AM parts is a major drawback to their aesthetics and functionality. Over the years, different approaches were proposed to enhance their surface quality, each bearing its limitations. Among the proposed technologies, electropolishing is a strong candidate for improving the surface finish of AM parts. This study aims to review the literature on electropolishing of AM parts. However, to provide a comprehensive study of the different aspects involved, a brief review is also presented on the origin and consequences of the surface properties of AM parts as well as an evaluation of other available post-treatment technologies. Finally, the existing challenges on the way and potential countermeasures to expedite the industrial application of the electropolishing process for post-treatment of AM parts as well as future research avenues are discussed.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".