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Record W3137709962 · doi:10.18280/acsm.450101

Influence of the Manufacturing Parameters in Selective Laser Melting on Properties of Aluminum Alloy AlSi7Mg0.6 (A357)

2021· article· en· W3137709962 on OpenAlexvenueno aff
Arnold Mauduit, Hervé Gransac, Sébastien Pillot

Bibliographic record

VenueAnnales de Chimie Science des Matériaux · 2021
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing Materials and Processes
Canadian institutionsnot available
FundersCentre Technique des Industries Mécaniques
KeywordsMaterials scienceAlloyKeyholeSelective laser meltingAnisotropyMicrostructureAluminiumLaserThermal conductionLaser power scalingThermalThermal conductivityEvaporationMaterial propertiesComposite materialOpticsThermodynamicsWelding

Abstract

fetched live from OpenAlex

Various selective laser melting (SLM) configurations (8 in all) were tested on aluminum alloy AlSi7Mg0.6 by making single tracks on parallelepipeds specimens. We used an energy balance as a means of connecting the machine parameters (power, speed, etc.) of the 8 configurations to the morphology (geometry) of the single tracks. On this basis, we correlated the width, depth and especially the section area of the melt pool (single track) to the linear energy density. We were also able to assess the absorption coefficient of the aluminum alloy AlSi7Mg0.6 as a function of the temperature. The study was then focused on the microstructure and the possible impacts on the material properties including on the mechanical characteristics and the anisotropy observed in literature based on the build direction. Evidence suggests that the Hall-Petch relation can be used to explain this anisotropy. The thermal analysis highlighted two laser operating modes: the keyhole mode and the conduction mode. These modes have also been described via the morphology of the single tracks. Finally, a comparison between Rosenthal’s theoretical model (in the case of the conduction mode) and actual conditions was proposed by the obtained geometry of the single tracks as well as the cooling speeds calculated and measured using the dendrite arm spacing (DAS). The maximum temperatures achieved were also assessed by Rosenthal’s theoretical model which made it possible to explain the evaporation of some chemical elements during the manufacturing of the aluminum alloy through SLM.

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 score0.590
Threshold uncertainty score0.472

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 studies0.0000.001
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 head0.023
GPT teacher head0.224
Teacher spread0.200 · 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

Citations10
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueAnnales de Chimie Science des MatériauxSame topicAdditive Manufacturing Materials and ProcessesFrench-language works237,207