Influence of laser polishing and chemical etching on surface roughness, microstructure, and hardness of as-built Ti-5Al-5Mo-5V-3Cr alloy produced by laser powder bed fusion
Bibliographic record
Abstract
Ti-5Al-5Mo-5V-3Cr (Ti-5553) parts are used in aerospace applications due to their excellent mechanical and corrosion resistance properties. However, to use additively manufactured Ti-5553 components effectively, surface quality must be improved. This study demonstrates the effective use of laser polishing on laser powder bed fusion (LPBF)-manufactured Ti-5Al-5Mo-5V-3Cr (Ti-5553) parts, resulting in a significant reduction in surface roughness. Initial laser polishing reduced the surface roughness (Sa) from 17.4 to 4.9 μm, with subsequent chemical etching further lowering it to 4.1 μm. Across all tested conditions, Sa values remained within an acceptable range, from 4.9 to 9.7 μm. Microstructural analysis after laser polishing revealed characteristic wave patterns with some discontinuities, while chemical etching made the grain boundaries more visible. A slight reduction in hardness in the laser-polished samples suggests stress relief in the as-built samples, and transmission electron microscopy investigation confirmed that no phase transformation occurred at the polished surface. These findings underscore the effectiveness of laser polishing in enhancing the surface quality of LPBF-manufactured Ti-5553 parts for various applications.
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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 | 0.000 | 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".