Effects of Laser-Powder Bed Fusion Process Parameters on the Microstructure and Corrosion Properties of AlSi10Mg Alloy
Bibliographic record
Abstract
In this study, the effects of microstructural modifications induced by tuning the laser-powder bed fusion (L-PBD) process parameters on electrochemical stability of the L-PBF-AlSi10Mg alloy are investigated. Three groups of L-PBF-AlSi10Mg samples were fabricated utilizing combinations of L-PBF process parameters for their Upskin layers. The implemented process parameters modifications were found to be not only effective in reducing the as-printed surface roughness of the components, but also led to the formation of cyclic small-large melt pools (MPs) in Upskin layers of the fabricated samples. Such consecutive modification in the size of MPs led to the increased inhomogeneity of the microstructure, contributing to the formation of a coarser intercellular eutectic-Si network, larger grain size, and lower density of low angle grain boundaries. Among all fabricated samples, the sample that experienced the fastest cooling during solidification was found to reveal the highest corrosion resistance and the best passive film stability on its Upskin surface both in naturally-aerated and deaerated 3.5 wt.% NaCl electrolyte, owing to the finer Al-Si eutectic structure that forms along its large MP-boundaries. The as-printed microstructure of the L-PBF-AlSi10Mg was found a dominant factor in determining the necessity of applying post-printing surface polishing procedures to attain better corrosion properties.
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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 | 0.000 | 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".