Review—Electropolishing of Additive Manufactured Metal Parts
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 it