MétaCan
Menu
Back to cohort
Record W4402419586 · doi:10.1016/j.msea.2024.147249

Evolution of mechanical properties and microstructure of selective laser melted AlSi10MgMn alloy with different post heat treatments

2024· article· en· W4402419586 on OpenAlexafffund
Esmaeil Pourkhorshid, Paul Rometsch, X.-Grant Chen

Bibliographic record

VenueMaterials Science and Engineering A · 2024
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing Materials and Processes
Canadian institutionsRio Tinto (Canada)Université du Québec à Chicoutimi
FundersNatural Sciences and Engineering Research Council of CanadaCentre québécois de recherche et de développement de l’aluminium
KeywordsMicrostructureAlloyMaterials scienceMetallurgyLaserComposite materialOpticsPhysics

Abstract

fetched live from OpenAlex

This study investigated the effects of various post heat treatments on the mechanical properties and microstructure evolution of an AlSi10MgMn alloy containing 0.5 wt% Mn produced by the selective laser melting process for the first time. The microstructures under different conditions were analyzed using optical microscopy, scanning electron microscopy, electron backscatter diffraction, and transmission electron microscopy. In the as-manufactured (F) condition, the alloy exhibited an ultimate tensile strength (UTS) of 486 MPa, a yield strength (YS) of 299 MPa, and an elongation of 10.3 %. After a T5 treatment, the UTS and YS increased to 532 MPa and 386 MPa, respectively, resulting in a remarkable 30 % improvement in YS compared to the F state. The tensile properties achieved by the new alloy were considerably higher than those reported for conventional AlSi10Mg alloys in the F, T5, and T6 conditions. The T5 treatment promoted the precipitation of a large fraction of Si-rich nanoparticles and MgSi-based precipitates without disrupting the Si-rich network. After a T6 treatment, the Si-rich network completely disappeared, and the main strengthening phase was MgSi-based precipitates accompanied by α-Al(Mn,Fe)Si dispersoids induced by the Mn addition. Using microstructure-based constitutive models, the strengthening contributions of various microstructural components to mechanical strength in different processing conditions were analyzed.

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 score0.008
Threshold uncertainty score0.386

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 head0.006
GPT teacher head0.176
Teacher spread0.170 · 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
Published2024
Admission routes2
Has abstractyes

Explore more

Same venueMaterials Science and Engineering ASame topicAdditive Manufacturing Materials and ProcessesFrench-language works237,207