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Record W4403548255 · doi:10.1016/j.heliyon.2024.e39522

Effect of energy density on down surface characteristics of AlSi10Mg alloy fabricated via selective laser melting

2024· article· en· W4403548255 on OpenAlexaff
Cheol Kang, Gun-Hee Kim, Won Rae Kim, Taeg Woo Lee, Hyun Soo Kang, Min Ji Ham, Seon‐Jin Choi, Hye Yoon Choi, Ki-Seung Kim, Hyo Tae Kim, Hyung Giun Kim

Bibliographic record

VenueHeliyon · 2024
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing Materials and Processes
Canadian institutionsNexen (Canada)
FundersKorea Institute of Industrial Technology
KeywordsSelective laser meltingAlloyMaterials scienceLaserEnergy densityMetallurgyComposite materialEngineering physicsOpticsMicrostructureEngineeringPhysics

Abstract

fetched live from OpenAlex

The correlation between surface roughness and energy density in the down surface area of AlSi10Mg alloy manufactured by selective laser melting was analyzed. This study investigated the relationship between the contour melt pool shape and surface roughness in the down surface area across an energy density range of 10-150 J/mm³. As the energy density increased, the contour melt pool in the down surface area became more stable, which significantly influenced surface roughness. Low energy density resulted in the unstable formation of the contour melt pool, leading to a deterioration in surface quality, whereas high energy density promoted the stable formation of the melt pool. Sufficient energy density is essential for the complete formation of the contour melt pool on the down surface, which plays a crucial role in reducing surface roughness. However, within the energy density range where the contour melt pool is fully formed, keyhole defects may occur, and it can be anticipated that these defects may worsen at energy densities exceeding the critical threshold.

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 score0.022
Threshold uncertainty score0.578

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 head0.005
GPT teacher head0.207
Teacher spread0.203 · 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

Citations3
Published2024
Admission routes1
Has abstractyes

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