Effect of particle size distribution on the sidewall surface roughness of AlSi10Mg parts manufactured by laser powder bed fusion
Bibliographic record
Abstract
The sidewall surface roughness is believed to be caused by unmelted or partially melted powders adhering to the meltpool, and thus can be linked to the particle size distribution (PSD) and apparent density of the feedstock. This study reports the effect of PSD, apparent density and sidewall coordination number on sidewall surface roughness from metal Laser Powder Bed Fusion (LPBF) processing. A series of thin walls were fabricated using four different AlSi10Mg alloy powders to investigate this correlation. The results indicate that a large powder size (D 10 , D 50 , D 90 of 74, 82, and 104 µm, respectively) combined with a narrow PSD (S w of 4.28) reduces the number of contacts with the sidewall, thereby decreasing the number of particles attached to the surface, resulting in the reduction of the surface roughness parameters, S a, and S q , by up to 43.4 % and 24.8 % respectively. This work advances the understanding of the factors driving surface roughness in LPBF and demonstrates that the usage of a proper PSD can significantly improve surface quality in metal AM processes.
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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".