A multi-physics and multi-scale approach to characterize the viscous layer growth formed on SLM 316 L stainless steel during electropolishing in an acid mixture
Bibliographic record
Abstract
This study aims to provide experimental evidence of the formation and the growth of a viscous layer during the electropolishing of additively manufactured 316 L stainless steel parts, using a combination of optical characterization techniques. A tertiary current distribution model was subsequently developed to simulate the electropolishing process and predict the evolution of the viscous layer. The results demonstrate that the viscous layer forms and grows within a specific potential window before being disrupted by gas evolution due to solvent oxidation. Schlieren imaging estimates the thickness of the layer to be approximately 1.4 mm after 5 min of polarization at the onset of the polishing plateau—about 1 mm thicker than its natural state without polarization. Particle Image Velocimetry (PIV) confirms the presence of a flow-deprived zone near the surface, roughly 1 mm thick, contrasting with the bulk region where natural convection dominates. A simplified reaction mechanism is proposed, based on experimentally determined electron-transfer kinetics. Metal cations are assumed to be instantly complexed, with the diffusion of the resulting complexes considered equivalent to that of the free complexing agents (phosphates), based on literature values. Using these assumptions, the tertiary current distribution model successfully replicates the growth of the viscous layer. The model's predictions were validated by experimental measurements of metal cation concentrations, supporting the hypothesis that the diffusion of “acceptor” species is the primary driving force behind electropolishing. This work also confirms that phosphate-complexed metal cations diffuse analogously to anions.
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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 | 0.000 |
| 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 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".