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Record W2080863251 · doi:10.1179/174329009x409723

Effects of cold and hot densification on the mechanical properties of a 7XXX series powder metallurgy alloy

2009· article· en· W2080863251 on OpenAlexafffund
I. A. MacAskill, A. D. P. LaDepha, J. Milligan, J. J. Fulton, D.P. Bishop

Bibliographic record

VenuePowder Metallurgy · 2009
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloy Microstructure Properties
Canadian institutionsDalhousie University
FundersAUTO21 Network of Centres of ExcellenceNatural Sciences and Engineering Research Council of Canada
KeywordsSwagingMaterials scienceAlloyUltimate tensile strengthMetallurgyPowder metallurgyHot workingMicrostructure

Abstract

fetched live from OpenAlex

The objective of this work was to investigate the effects of hot and cold swaging on the density and mechanical properties of a commercial Al–Zn–Mg–Cu powder metallurgy alloy known as Alumix 431D. To do so, as sintered samples of the PM alloy were swaged under a variety of conditions and characterised. For comparison purposes, equivalent characterisation tests were completed on the chemically similar wrought alloy 7075-T6. Cold swaging was moderately successful provided the as sintered billets were annealed or solutionised before densification. Here, modest improvements in density and tensile properties were noted. Hot swaging proved to be a more effective approach. Optimal properties were achieved when samples were preheated to 470 ± 10°C. When processed in this manner, a density of 99·6% of theoretical was realised while the tensile and fatigue properties exceeded those of the wrought 7075-T6 alloy tested for comparison purposes.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.011
GPT teacher head0.182
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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), 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

Citations28
Published2009
Admission routes2
Has abstractyes

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