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Record W2610623086 · doi:10.1016/j.proeng.2017.04.144

Effect of Superheating Melt Treatment on Mg 2 Si Particulate Reinforced in Al-Mg 2 Si-Cu In situ Composite

2017· article· en· W2610623086 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueProcedia Engineering · 2017
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloys Composites Properties
Canadian institutionsUniversity of Ottawa
FundersUniversiti Teknologi Malaysia
KeywordsSuperheatingMaterials scienceComposite numberNucleationComposite materialParticulatesPhase (matter)ThermalIn situMetallurgyChemistry

Abstract

fetched live from OpenAlex

As one of the advanced engineering materials, Al-based composite reinforced with Mg 2 Si phase has been assigned to be a potential candidate in the manufacture of automotive products, especially those that require high temperature applications. Superheating melt treatment with various temperatures and holding times are shown to cause alteration of primary Mg 2 Si reinforced particulate that subsequently would improve the mechanical properties of the in situ composite. This was investigated via microstructural and thermal analysis observation. Superheat temperature at 950 °C with 15 minutes holding time has presented an adequate modification effect with skeleton structure of Mg 2 Si particles faded and transformed into fine polygonal shape, accompanied with decrease in size. In addition, the thermal analysis result has shown increment in nucleation temperature, T N compared to unmodified composite indicating the modification of particles is allocated with this composite melt treatment. This modification of structure is believed capable to enhance the strength properties of the in situ composite that could meet the application requirements.

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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.043
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.008
GPT teacher head0.219
Teacher spread0.211 · 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