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Record W4386629369 · doi:10.3390/engproc2023043008

Surface Roughness Studies on F357 Aluminum Alloy Fabricated Using Laser Powder Bed Fusion Process

2023· article· en· W4386629369 on OpenAlexaff
Jaskaranpal Singh Dhillon, Su Su, Oscar Sanchez Mata, T. S. Ramakrishnan, Mathieu Brochu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing Materials and Processes
Canadian institutionsMcGill University
Fundersnot available
KeywordsMaterials scienceSurface roughnessAlloyCube (algebra)Surface finishAluminiumFusionLaserMetallurgyScanning electron microscopeComposite materialOpticsGeometry

Abstract

fetched live from OpenAlex

Aluminum alloys processed using laser powder bed fusion are attracting industrial and research efforts striving to achieve the lightweighting potential and combination of good mechanical and corrosion properties. In this study, border parameter optimization for F357 (AlSi7Mg) alloy processed using LPBF was performed with the aim of reducing the as-fabricated surface roughness. The optimization revealed that laser power and scanning speed can significantly influence the surface roughness of cube vertical surfaces. Measured areal average surface roughness (Sa) for cube samples varied from 15 to 24 µm. The cube-based optimized parameters were used to fabricate angled wall samples with angles ranging from 45° to 90°. The surface roughness reduced when the built angle increased from 45° to 75°, while for 75–90°, the surface roughness values remained constant.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.879
Threshold uncertainty score0.838

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 head0.047
GPT teacher head0.294
Teacher spread0.248 · 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
Published2023
Admission routes1
Has abstractyes

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