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Record W2085477337 · doi:10.1115/1.1651096

Influence of Carbon Content in Cobalt-Based Superalloys on Mechanical and Wear Properties

2004· article· en· W2085477337 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Engineering Materials and Technology · 2004
Typearticle
Languageen
FieldEngineering
TopicAdvanced materials and composites
Canadian institutionsNational Research Council CanadaCarleton University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceMicrostructureUltimate tensile strengthCarbon fibersTribometerDifferential scanning calorimetrySuperalloyComposite materialMetallurgyCobaltFracture (geology)Phase (matter)Flexural strengthStress (linguistics)TribologyComposite number

Abstract

fetched live from OpenAlex

Two cobalt-based superalloys containing 1.6% and 2% carbon respectively were studied, with the emphasis on the influence of the carbon content on their microstructures, wear resistance, and mechanical properties. Phase formation and transformation in the microstructures were analyzed using metallographic, X-ray diffraction, and differential scanning calorimetry techniques. Wear resistance, tensile and fatigue behaviors of the alloys were investigated on a pin-on-disc tribometer, MTS machine and rotating-bending machine, respectively. It is found that the wear resistance was increased significantly with the carbon content. The mechanical properties of the alloys are also influenced by the carbon content, but the impact is not so significant as on the wear resistance. It was observed that the carbon content increased the yielding strength and fatigue strength, but decreased the fracture stress and fracture strain.

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)0.000
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 score0.159
Threshold uncertainty score0.346

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 head0.173
Teacher spread0.165 · 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