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Record W2096158160 · doi:10.1115/1.2840959

Modeling Strain Localization Using a Plane Stress Two-Particle Model and the Influence of Grain Level Matrix Inhomogeneity

2008· article· en· W2096158160 on OpenAlexafffund
Xiaohua Hu, David S. Wilkinson, Mukesh Jain, Raja K. Mishra

Bibliographic record

VenueJournal of Engineering Materials and Technology · 2008
Typearticle
Languageen
FieldEngineering
TopicMetal Forming Simulation Techniques
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaGeneral Motors of Canada
KeywordsMaterials scienceMatrix (chemical analysis)Particle (ecology)HomogeneousStress (linguistics)Condensed matter physicsStrain (injury)Composite materialPhysicsStatistical physics

Abstract

fetched live from OpenAlex

The role of dilute small particles on the development of strain localization under uniaxial tension has been studied by finite element analysis using a plane stress model with two small hard particles embedded in Al matrix. The influence of particle alignment and interparticle spacing in a homogeneous and inhomogeneous matrix are investigated. When the matrix material is a homogeneous continuum, there are small localization strains when close packed and aligned along the loading direction. In the case of an inhomogeneous matrix with grains of different strengths represented by their Taylor factors, the location of localization band is insensitive to the interparticle spacing, but mainly determined by grain-level inhomogeneity. This is because the particles are dilute and small compared with the matrix grains. The particles, however, can decrease the localization strains when they straddle the localization band.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0020.001
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 head0.021
GPT teacher head0.248
Teacher spread0.227 · 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 designSimulation or modeling
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

Citations14
Published2008
Admission routes2
Has abstractyes

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