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Record W2801855080 · doi:10.1139/tcsme-2006-0020

OPTIMIZING THE EFFECTIVE PARAMETERS OF TUNGSTEN – COPPER COMPOSITES

2006· article· en· W2801855080 on OpenAlex
K. Daneshjou, Morteza Ahmadi

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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2006
Typearticle
Languageen
FieldEngineering
TopicAdvanced materials and composites
Canadian institutionsnot available
Fundersnot available
KeywordsMaterials scienceComposite materialTungstenCopperPressingElectrical resistivity and conductivityWeldingHot pressingMetallurgy

Abstract

fetched live from OpenAlex

Tungsten – Copper composites are generally used for electrical contact materials. These composites are suitable for hard working conditions such as intensive electrical sparks, gouging spark erosion, surface melting, welding, material transfer etc. The aim of the present article is to determine optimum processing conditions to improve the mechanical and physical properties of W-Cu composites with the view to increase their lifetime. W-Cu specimens are produced using powder material and the liquid infiltration process. Chemical composition, pressing machine pressure, infiltration time and temperature are variable parameters for specimen production. By optimizing the amount of cobalt addition, shaping pressure, time and temperature of production process, optimum values for mechanical and electrical properties such as density, hardness and resistivity are obtained.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.793
Threshold uncertainty score0.440

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 head0.005
GPT teacher head0.175
Teacher spread0.171 · 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