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Record W2781545579 · doi:10.3139/146.111591

Intermetallic precipitation in rare earth-treated A413.1 alloy: A metallographic study

2018· article· en· W2781545579 on OpenAlexaff
A. M. Samuel, H. W. Doty, S. Valtierra, F. H. Samuel

Bibliographic record

VenueInternational Journal of Materials Research (formerly Zeitschrift fuer Metallkunde) · 2018
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloy Microstructure Properties
Canadian institutionsUniversité du Québec à Chicoutimi
Fundersnot available
KeywordsMaterials scienceIntermetallicEutectic systemAlloyMetallurgyNucleationPrecipitationMicrostructureAnalytical Chemistry (journal)Thermodynamics

Abstract

fetched live from OpenAlex

Abstract The present study was performed mainly on A413.1 alloy. Measured amounts of La, Ce or La+Ce, Ti and Sr were added to the molten alloy in the form of master alloys. Samples sectioned from castings obtained from thermal analysis experiments were used for preparing samples for metallographic examination. The results show that addition of rare earth ( RE ) metals to Al–Si alloys increased the α-Al nucleation temperature and depressed the Al-Si eutectic formation temperature, thereby increasing the solidification range. Depending upon the alloying elements/additives, a large number of RE -based intermetallics could be formed: Al 4 (Ce,La), Al 13 (Ce,La) 2 Cu 3, Al 7 (Cu,Fe) 6 (Ce,La) 6 Si 2, Al 4 La, Al 2 La 5 Si 2, Al 2 Ce 5 Si 2, Al 2 (Ce,La) 5 Si 2. Under an electron microscope, these phases appear in backscatter imaging mode in the form of thin grayish-white platelets on the dark gray Al matrix. The average thickness of these platelets is about 1.5 μm. When the alloy is grain refined with Ti-based master alloys, precipitation of a gray phase in the form of sludge is observed: Al 12 La 3 Ti 2 , or Al 12 (Ce,La) 3 Ti 2. Regardless the alloy composition, the RE /Al ratios remain constant in each type of intermetallic. Rare earth metals have a strong affinity to react with Sr (resulting in partial modification of the eutectic Si particles) as well as some transition elements, in particular Ti and Cu. Iron has a very low affinity for interaction with RE metals. It is only confined to Fe-based intermetallics.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.146
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0020.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.037
GPT teacher head0.327
Teacher spread0.290 · 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.

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

Citations8
Published2018
Admission routes1
Has abstractyes

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