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Application of the Three-Dimensional Damage Percolation Model and X-Ray Tomography for Damage Evolution Prediction in Aluminium Alloys

2006· article· en· W1980202303 on OpenAlexaff
O. S. Orlov, Éric Maire, Jérôme Adrien, Michael J. Worswick, David J. Lloyd

Bibliographic record

VenueMaterials science forum · 2006
Typearticle
Languageen
FieldEngineering
TopicMetal Forming Simulation Techniques
Canadian institutionsNovelis (Canada)University of Waterloo
Fundersnot available
KeywordsMaterials scienceCoalescence (physics)NucleationVoid (composites)AluminiumAlloyUltimate tensile strengthTomographyTensile testingPercolation (cognitive psychology)Composite materialThermodynamicsOptics

Abstract

fetched live from OpenAlex

A three-dimensional damage percolation model, which captures the effect of microstructural heterogeneity on damage evolution, has been developed to model damage initiation and propagation in materials containing second phase particles. It considers the three phenomena preceding ductile rupture of the material: void nucleation, growth, and coalescence. Threedimensional X-ray tomography is used to obtain measured three-dimensional second phase particle distributions in aluminum alloy sheet. Material damage evolution is studied within a tensile test simulation and compared to measured damage from an in situ tensile test utilizing X-ray tomography. Experimental and simulation results for material damage initiation and evolution are in good agreement.

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: Methods · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.000
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 head0.215
Teacher spread0.209 · 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
GenreMethods

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

Citations1
Published2006
Admission routes1
Has abstractyes

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