A Novel Hybrid Ultrasound Abrasive-Driven Electrochemical Surface Finishing Technique for Additively Manufactured Ti6Al4V Parts
Bibliographic record
Abstract
Poor surface quality is one of the drawbacks of metal parts made by additive manufacturing (AM)—they normally possess relatively high surface roughness and different types of surface irregularities. Post-processing operations are usually needed to reduce the surface roughness to have ready-to-use parts. Among all the surface treatment techniques, electrochemical polishing has the highest finishing efficiency and flexibility. However, although the average surface roughness can be reduced effectively (more than 80% roughness reduction), large-scale surface waviness still remains an issue when finishing metal AM parts. To maintain the finishing efficiency while reducing the surface waviness, a novel hybrid surface finishing technique is designed, which involves the combination of electropolishing, ultrasonic vibration, and abrasion. Preliminary experiments to prove the feasibility of novel hybrid finishing methods were conducted on Ti6Al4V coupons manufactured via laser powder bed fusion (LPBF). Electropolishing, a combination of ultrasound and abrasion, and hybrid finishing were conducted for process optimization and comparison purposes. The effects of the voltage, inter-electrode gap, temperature, ultrasonic amplitude, abrasive concentration, and processing time were studied and optimized. When similar optimal arithmetic mean height values (Sa ≈ 1 μm) are achieved for both processes, the arithmetic mean waviness values (Wa) obtained from hybrid finishing are much less than those from sole electropolishing after the same processing time, with the amount being 61.7% less after 30 min and 40.0% after 45 min.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".