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Record W3095243547 · doi:10.3390/ma13214849

Parametric Study on In Situ Laser Powder Bed Fusion of Mo(Si1−x,Alx)2

2020· article· en· W3095243547 on OpenAlexaff
Tatevik Minasyan, Sofiya Aydinyan, Ehsan Toyserkani, Irina Hussainova

Bibliographic record

VenueMaterials · 2020
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing Materials and Processes
Canadian institutionsUniversity of Waterloo
FundersEesti Teadusagentuur
KeywordsIn situFusionMaterials scienceLaserParametric statisticsMetallurgyOpticsChemistryMathematicsPhysicsOrganic chemistryStatistics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.710

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.023
GPT teacher head0.234
Teacher spread0.211 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2020
Admission routes1
Has abstractyes

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