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Record W2030267951 · doi:10.12989/amr.2013.2.4.221

Mechanical and wear properties of Cu-Al-Ni-Fe-Sn-based alloy

2013· article· en· W2030267951 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAdvances in materials Research · 2013
Typearticle
Languageen
FieldMaterials Science
TopicMetallurgy and Material Science
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMaterials scienceAlloyDuctility (Earth science)MetallurgyUltimate tensile strengthMicrostructureBrittlenessBronzePlasticityComposite material

Abstract

fetched live from OpenAlex

To obtain bronze with good mechanical properties and high wear resistance, a new bronze (CADZ) is proposed on the basis of various fundamental information. The CADZ consists of the elements Al10.5, Fe4.2, Sn3.7 and Ni3.1, and its design is based on Cu-Al10.5 alloy. The Cu-10.5%Al is very hard and brittle. To obtain the high material ductility of the Cu-10.5%Al alloy, an attempt was made to add a few percent of Sn. Moreover, to make high strength of the Cu alloy, microstructure with small grains was created by the proper amount of Fe and Ni (Fe/Ni = 0.89). The mechanical properties of the CADZ sample have been examined experimentally, and those were compared with commercial bronzes. The tensile strength and wear resistance of CADZ are higher than those for commercial bronzes. Although the ductility of CADZ is the lower level, the strain to failure of CADZ is about 2.0~5.0% higher than that for the Cu-Al10.5 alloy. Details of the microstructural effects on the mechanical properties in the CADZ sample were further discussed using various experimental results.

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.

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 categoriesInsufficient 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 score0.004
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.064
GPT teacher head0.360
Teacher spread0.296 · 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