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Record W2564406664 · doi:10.1155/2016/7691535

Effect of Sr-P Interaction on the Microstructure and Tensile Properties of A413.0 Type Alloys

2016· article· en· W2564406664 on OpenAlexaff
A. M. Samuel, H. W. Doty, S. Valtierra, F. H. Samuel

Bibliographic record

VenueAdvances in Materials Science and Engineering · 2016
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloy Microstructure Properties
Canadian institutionsUniversité du Québec à Chicoutimi
Fundersnot available
KeywordsMaterials scienceEutectic systemPrecipitationStrontiumUltimate tensile strengthDissolutionMicrostructureSiliconMagnesiumMetallurgyChemical engineering

Abstract

fetched live from OpenAlex

The present study was performed on low magnesium A413.0 type alloys. The results show that strontium (Sr) is mainly concentrated in the silicon particles. Overmodification occurs when Sr precipitates in the form of Al2SrSi2, which takes place over a wide range of temperatures. The first peak occurs following the precipitation ofα-Al, the second peak is merged with the precipitation of eutectic silicon (Si), and the third peak is a posteutectic reaction. Introduction of phosphorus (P) to Sr-modified alloys leads to the formation of (Al,P,Sr)2O5compound, which reduces the modification effectiveness of Sr. Therefore, in the presence of P, the amount of added Sr should exceed 200 ppm. For the same levels of P, the tensile parameters of well modified alloys (233 ppm Sr) are relatively higher than those partially modified with Sr (about 60 ppm Sr) containing the same amount of P. During solution heat treatment, coarsening of the eutectic Si particles occurs by the growth of some particles at the expense of the dissolution of the smaller ones, as well as by the collision of nearby particles.

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 score0.001
Threshold uncertainty score0.002

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)0.0010.000

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 head0.005
GPT teacher head0.202
Teacher spread0.198 · 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

Citations12
Published2016
Admission routes1
Has abstractyes

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Same venueAdvances in Materials Science and EngineeringSame topicAluminum Alloy Microstructure PropertiesFrench-language works237,207