Effect of Mn on microstructural characteristics and mechanical behavior of AlSi10Mg alloys produced by laser powder bed fusion
Bibliographic record
Abstract
This study investigated the effect of Mn modification on the microstructure and mechanical properties of AlSi10Mg alloys produced by laser powder bed fusion (L-PBF). The results revealed that the addition of 0.5 wt% Mn considerably improved the strength while maintaining a similar elongation level, exhibiting yield strength increases of 17 %, 30 % and 29 % in the as-manufactured (F), directly aged (T5), and solution treated plus aged (T6) conditions, respectively. In the F and T5 conditions, the Mn-modification resulted in the formation of α-Al(Mn,Fe)Si intermetallic particles inside the Si-rich network, which reinforced the network and improved the strength. After the T6 heat treatment, the Si-rich network completely disappeared in both alloys, but the formation of α-Al(Mn,Fe)Si dispersoids provided an extra strengthening contribution in the Mn-modified alloy. Most importantly, transmission electron microscopy and atom probe tomography revealed that the addition of Mn (and some extra Mg) stimulated the precipitation of a large number of Si-rich nanoparticles and MgSi-based precipitates, especially in the T5 condition. Among all the heat treatment and alloy conditions investigated, the Mn-modified alloy in the T5 condition achieved the highest strengths (yield strength: 386 MPa, ultimate tensile strength: 532 MPa). This research highlights the potential for improving the mechanical properties of AlSi10Mg alloys produced by L-PBF via a cost-effective modification of the chemical composition and provides a deeper understanding of the role of Mn in such alloys.
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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".