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Record W1994931898 · doi:10.1115/1.2841619

Dynamic Dislocation-Defect Analysis and SAXS Study of Nanovoid Formation in Aluminum Alloys

2008· article· en· W1994931898 on OpenAlexafffund
L. J. Westfall, B.J. Diak, M. A. Singh, S. Saimoto

Bibliographic record

VenueJournal of Engineering Materials and Technology · 2008
Typearticle
Languageen
FieldMaterials Science
TopicMicrostructure and mechanical properties
Canadian institutionsQueen's University
FundersBasic Energy SciencesNatural Resources CanadaNatural Sciences and Engineering Research Council of CanadaGeneral Motors of CanadaU.S. Department of EnergyOffice of ScienceNational Science Foundation
KeywordsSmall-angle X-ray scatteringMaterials scienceVoid (composites)AluminiumDislocationVolume fractionScatteringCrystallographyComposite materialMetallurgyOpticsChemistry

Abstract

fetched live from OpenAlex

Crystalline defects other than the essential dislocations are produced by dislocation intersections resulting in debris, which can transform into loops, point defects, and∕or nanovoids. The stress concentrations ahead of slip clusters promote void formation leading to incipient cracks. To evaluate the progression of these processes during deformation, dynamic dislocation-defect analysis was applied to nominally pure aluminum, Al–Mg, and Al–Cu alloys. In the case of nanovoid formation, small angle X-ray scattering (SAXS) was used to quantitatively assess if the void size and its volume fraction can be determined to directly correlate with the measured thermodynamic response values. The SAXS signal from the nanovoids in nominally pure aluminum is distinctly measurable. On the other hand, thermomechanical processing of even nominally pure aluminum results in the formation of nanoprecipitates, which requires future calibration.

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 score0.002

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)0.0000.000

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 head0.006
GPT teacher head0.201
Teacher spread0.195 · 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

Citations7
Published2008
Admission routes2
Has abstractyes

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