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Record W3197656432 · doi:10.3390/ma14185188

Performance Testing and Rapid Solidification Behavior of Stainless Steel Powders Prepared by Gas Atomization

2021· article· en· W3197656432 on OpenAlexaff
H. Jerry Qi, Xianglin Zhou, Jinghao Li, Yunfei Hu, Lianghui Xu

Bibliographic record

VenueMaterials · 2021
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing Materials and Processes
Canadian institutionsMcGill University
FundersNational Key Research and Development Program of ChinaNational Natural Science Foundation of China
KeywordsMaterials scienceMicrostructureMetal powderPowder metallurgyNucleationSphericityMetallurgyAlloyRaw materialComposite materialMetal

Abstract

fetched live from OpenAlex

Gas atomization is a widely used method to produce the raw powder materials for additive manufacturing (AM) usage. After the metal alloy is melted to fusion, gas atomization involves two relatively independent processes: liquid breakup and droplet solidification. In this paper, the solidification behavior of powder during solidification is analyzed by testing the powder’s properties and observing microstructure of a martensitic stainless steel (FeCrNiBSiNb). The powder prepared by gas atomization has high sphericity and smooth surface, and the yield of qualified fine powder is 35%. The powder has typical rapid solidification structure. Collision between powders not only promotes nucleation, but also produces more satellite powder. The segregation of elements in powder is smaller as the result of high cooling rate which can reaches 4.2 × 105 K/s in average. Overall, the powder prepared by gas atomization is found to have good comprehensive properties, desired microstructure, and accurate chemical component, and it is suitable for various additive manufacturing techniques.

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 score0.006
Threshold uncertainty score0.429

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)0.0000.000

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 head0.014
GPT teacher head0.206
Teacher spread0.192 · 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

Citations18
Published2021
Admission routes1
Has abstractyes

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