Electropolishing of Laser Powder Bed-Fused IN625 Components in an Ionic Electrolyte
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
This work presents the first practical application of ionic electrolytes for electropolishing of nickel-based superalloys. It contains the results of an experiment-driven optimization of the applied potential and electrolyte temperature during electropolishing of laser powder bed-fused IN625 components containing surfaces oriented to the building platform under angles varying from 0 to 135°. For comparative purposes, the roughness profilometry and confocal microscopy techniques were used to characterize the surface finish topographies and the material removal rates of IN625 components subjected to electropolishing in ionic and acidic (reference) electrolytes. After 4 h of electropolishing in both electrolytes, a roughness of Ra ≤ 6.3 µm (ISO N9 grade number of roughness) was obtained for all the build orientations. To elaborate, both electrolytes manifested identical roughness evolutions with time on the 45° (75% Ra reduction) and 90°-oriented (65% Ra reduction) surfaces. Although the roughness reduction on the 135°-oriented surface in the ionic electrolyte was 17% less than in the acidic electrolyte, the former provided a more uniform roughness profile on the 0°-oriented surface (30% Ra reduction) and 44% higher current efficiency than the acidic electrolyte. This work proves that ionic electrolytes constitute a greener alternative to industrial acidic mixtures for electropolishing of three-dimensional (3D)-printed parts from nickel-based superalloys.
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 | 0.001 |
| 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 it