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Effect of Solution Heat Treatment and Additives on the Microstructure of Al – Si (A413.1) Automotive Alloys

2004· article· en· W2045850750 on OpenAlexaff
M A Moustafa, F. H. Samuel, H. W. Doty

Bibliographic record

VenueMaterials science forum · 2004
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloy Microstructure Properties
Canadian institutionsUniversité du Québec à Chicoutimi
Fundersnot available
KeywordsMaterials scienceMicrostructureAlloyEutectic systemElectron microprobeMetallurgyQuenching (fluorescence)Optical microscopeDendrite (mathematics)Phase (matter)Scanning electron microscopeCastingComposite material

Abstract

fetched live from OpenAlex

A study was carried out to determine the role of additives such as Mg and Cu on the microstructural characteristics of grain refined, Sr-modified eutectic A413.1 alloy (Al-11.7% Si) during solution heat treatment. For comparison purposes, some of the alloys were also studied in the non-modified condition. The alloys were cast in a steel permanent mold preheated at 425 °C that provided a microstructure with an average dendrite arm spacing (DAS) of ~ 22 µm. Castings were solution heat treated at 500 ± 2 °C for time up 24 h, followed by quenching in warm water (at 60 °C). Microstructural analysis of the as-cast and heat-treated castings was carried out using optical microscopy in conjunction with image analysis. Phase identifications were done using the electron probe microanalysis (EPMA) technique. In the as-cast condition, the addition of 0.42 wt% Mg to the unmodified alloy produced relatively large Si particles compared to the base A413.1 alloy. The Si particle size remained more or less the same with increase in solution treatment time and Mg level. Both Mg2Si and Al2Cu phases were observed to dissolve almost completely after 8 h solution time, while the Al5Cu2Mg8Si6 phase was found to persist even after 24 h.

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 score0.002
Threshold uncertainty score0.416

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 head0.005
GPT teacher head0.208
Teacher spread0.204 · 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

Citations27
Published2004
Admission routes1
Has abstractyes

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