MétaCan
Menu
Back to cohort
Record W3012999355 · doi:10.1002/srin.201900534

Production and Anisotropic Tensile Properties of Stainless Steel Wire Mesh/ZA8 Alloy Composites Produced by Squeeze Casting

2020· article· en· W3012999355 on OpenAlexaff
Bibo Yao, Zhaoyao Zhou, Zengtao Chen, Junwen Wang

Bibliographic record

Venuesteel research international · 2020
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloys Composites Properties
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMaterials scienceUltimate tensile strengthComposite materialAlloyComposite numberMicrostructureDuctility (Earth science)AnisotropyCastingCreep

Abstract

fetched live from OpenAlex

Novel stainless steel/zinc alloy composites are produced by infiltrating ZA8 zinc alloy into rolled 304 stainless steel wire mesh preforms via squeeze casting. Microstructural characteristics of the composite are observed, and the uniaxial tensile properties at different temperatures are investigated. The tensile properties of the composites of sintered preforms are compared with those of the composites of unsintered preforms. The results indicate that the microstructures and tensile behavior of the composites exhibit anisotropy. The tensile strength of the composite in the longitudinal direction is higher than that in the radial direction and increases with increasing wire fraction. The tensile strength and elongation at the maximum stress of the composite are lower than those of the unreinforced ZA8 alloy tested at room temperature. At an elevated temperature, the tensile strength of the alloy is improved but ductility decreases after adding wire mesh with wire fraction more than a certain value. The strength and elastic modulus of the alloy and composites sharply decrease with an increase in testing temperatures. The tensile strength of the composite decreases after sintering preforms. The fracture surface of the composite shows anisotropy in different directions and different morphologies at different temperatures.

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)0.001
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 score0.107
Threshold uncertainty score0.744

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.063
GPT teacher head0.277
Teacher spread0.214 · 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

Citations3
Published2020
Admission routes1
Has abstractyes

Explore more

Same venuesteel research internationalSame topicAluminum Alloys Composites PropertiesFrench-language works237,207