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Record W4313362615 · doi:10.3390/app13010430

Effect of Ti-B Grain Refiners on Wear and Corrosion of the A332 Alloy with Sr Modification

2022· article· en· W4313362615 on OpenAlexaff
Bruno Edu Arendarchuck, Andre R. Mayer, Willian Rafael de Oliveira, Anderson Geraldo Marenda Pukasiewicz, Luciano Augusto Lourençato, Hipólito Carvajal Fals, E. Martínez

Bibliographic record

VenueApplied Sciences · 2022
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloy Microstructure Properties
Canadian institutionsConcordia University
FundersUniversidade Federal do ParanáConselho Nacional de Desenvolvimento Científico e TecnológicoCoordenação de Aperfeiçoamento de Pessoal de Nível SuperiorUniversidade Tecnológica Federal do ParanáFundacion Araucaria
KeywordsMaterials scienceEutectic systemMetallurgyGrain sizeAcicularAlloyCorrosionIndentation hardnessCastingPhase (matter)Microstructure

Abstract

fetched live from OpenAlex

Grain refiners play a critical role in changing characteristics and properties of casting aluminum alloys. The Al-Si alloy (A332) is one of the most popular hypoeutectic alloys with a large range of industrial applications. It has a varied phase and morphology; however, it features problems with acicular-shaped eutectic phase, and generally exhibits dendritic cast grain type. To change this situation, the Sr element acts as a modifier of eutectic, which, along with a grain refiner may increase mechanical properties. In this work, two different grain refiners (Al5Ti1B, Al5Ti2B) were applied to the A332 alloy modified with Sr, and analyzed in relation to grain size, hardness, corrosion resistance, and wear behavior. Corrosion tests in 3.5 wt.% NaCl solution, nanoindentations, and Heyn’s method to analyze grain size and microhardness as optical and SEM images were made to examine the changes caused by grain refiners. A reduction in grain size was achieved, and the influence in size and hardness of the β-Fe phase was verified in the wear and corrosion analyses.

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 score0.016
Threshold uncertainty score0.153

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 studies0.0000.000
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 head0.007
GPT teacher head0.199
Teacher spread0.192 · 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

Citations12
Published2022
Admission routes1
Has abstractyes

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