Parametric Study on In Situ Laser Powder Bed Fusion of Mo(Si1−x,Alx)2
Bibliographic record
Abstract
Mo(Si1−x,Alx)2 composites were produced by a pulsed laser reactive selective laser melting of MoSi2 and 30 wt.% AlSi10Mg powder mixture. The parametric study, altering the laser power between 100 and 300 W and scan speed between 400 and 1500 mm·s−1, has been conducted to estimate the effect of processing parameters on printed coupon samples’ quality. It was shown that samples prepared at 150–200 W laser power and 400–500 mm·s−1 scan speed, as well as 250 W laser power along with 700 mm·s−1 scan speed, provide a relatively good surface finish with 6.5 ± 0.5 µm–10.3 ± 0.8 µm roughness at the top of coupons, and 9.3 ± 0.7 µm–13.2 ± 1.1 µm side surface roughness in addition to a remarkable chemical and microstructural homogeneity. An increase in the laser power and a decrease in the scan speed led to an apparent improvement in the densification behavior resulting in printed coupons of up to 99.8% relative density and hardness of ~600 HV1 or ~560 HV5. The printed parts are composed of epitaxially grown columnar dendritic melt pool cores and coarser dendrites beyond the morphological transition zone in overlapped regions. An increase in the scanning speed at a fixed laser power and a decrease in the power at a fixed scan speed prohibited the complete single displacement reaction between MoSi2 and aluminum, leading to unreacted MoSi2 and Al lean hexagonal Mo(Si1−x,Alx)2 phase.
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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) | 0.002 | 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".