Electropolishing of 316L stainless steel parts elaborated by selective laser melting: from laboratory to pilot scale
Bibliographic record
Abstract
Electropolishing is an effective technique for surface finishing of additively manufactured parts, compatible with complex geometries. It consists of an electrochemical dissolution in which the part to be treated is polarized anodically. The present study focuses on the development of an electrofinishing process dedicated to 316 L stainless steel elaborated by SLM. A study, performed at laboratory scale, allowed to characterize the electrochemical behavior of raw substrates (produced according to different laser scan strategies) and to define the bests operating conditions for the levelling (electrolyte composition, temperature, electrical parameters, duration…) with acceptable dissolution rates (around 5 µm/min). The transposition to a pilot unit able to process samples of several square centimeters (plates or tubes) requires a precise recalibration. Difficulties are essentially due to the high roughness of the SLM substrates (Ra ⋍30 µm, Rz ⋍ 200 µm), but also to issues related to the scale-up such as the current lines distribution that cause an inhomogeneous dissolution. To fit with the double objective of roughness decrease and geometrical integrity preservation, the use of pulsed potential shows an excellent efficiency. In such conditions, a 90% roughness decrease was measured while better preserving the shape integrity.
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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) | 0.000 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".