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Low-Pressure Casting of Aluminium AlSi7Mg03 (A356) in Sand and Permanent Molds

2020· article· en· W3096466740 on OpenAlexfundno aff
Franco Chiesa, Bernard Duchesne, Gheorghe Marin

Bibliographic record

VenueMATEC Web of Conferences · 2020
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloys Composites Properties
Canadian institutionsnot available
FundersMinistère de l'Économie, de la Science et de l'Innovation - Québec
KeywordsFoundryMaterials scienceAluminiumMetallurgyAlloyMoldCastingQuenching (fluorescence)Hardening (computing)WaxComposite materialLayer (electronics)

Abstract

fetched live from OpenAlex

Aluminium A356 (AlSi7Mg03) is the most common foundry alloy poured in sand and permanent molds or lost-wax shells. Because of its magnesium content, this alloy responds to a precipitation hardening treatment. The strength and ductility combination of the alloy can be varied at will by changing the temper treatment that follows the solutionizing and quenching of the part. By feeding the mold from the bottom, the low-pressure process provides a tranquil filling of the cavity. A perfect control of the liquid metal stream is provided by programming the pressure rise applied on the melt surface. It compares favorably to the more common gravity casting where a turbulent filling is governed by the geometry of the gating system.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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)0.0020.001

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 head0.017
GPT teacher head0.203
Teacher spread0.185 · 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 source (direct Gemma or distilled Codex), 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

Citations2
Published2020
Admission routes1
Has abstractyes

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Same venueMATEC Web of ConferencesSame topicAluminum Alloys Composites PropertiesFrench-language works237,207