Surface enhancement of stainless-steel parts produced by LPBF through finishing treatments
Bibliographic record
Abstract
Additive manufacturing (AM) technology has rapidly gained traction due to advances in AM processes, materials, and design research. Advantages of AM include improved ability to produce complex-shaped parts, operational flexibility, and shorter production times compared to conventional technologies. However, AM processes also suffer from some critical issues, such as low-quality surface and unsatisfactory mechanical performance. This is becoming increasingly important for medical applications where surface finish and roughness are critical. Therefore, various post-processing treatments are employed to enhance the surface quality of 3D-printed components. The present study, AISI 316L components fabricated via laser powder bed fusion were wire brush hammered with different numbers of passes: 5, 7, 10, and 15 passes. The surface quality was then examined by measuring roughness and microhardness. The results highlight the positive impact of this post-treatment on the surface quality. The surface roughness was significantly improved, decreasing by about 50%, from a starting roughness of 14 μm, attaining 6.5 μm after treatment. In addition, the microhardness increased significantly by about 102% from 202 Hv to 408 Hv. After 10 passes of wire brush hammering, the results stabilized, which means that the material reached a saturation point.
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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 | 0.000 | 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".