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Record W2939906506 · doi:10.4236/mnsms.2019.92002

Calculation of the Stress Intensity Factor in an Inclusion-Containing Matrix

2019· article· en· W2939906506 on OpenAlexafffund
Claude Lincourt, Jacques Lanteigne, Madhavarao Krishnadev, Carl Blais

Bibliographic record

VenueModeling and Numerical Simulation of Material Science · 2019
Typearticle
Languageen
FieldEngineering
TopicFatigue and fracture mechanics
Canadian institutionsUniversité LavalHydro-Québec
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsStress intensity factorFinite element methodMaterials scienceInclusion (mineral)StiffnessStructural engineeringMatrix (chemical analysis)Fracture (geology)Stress (linguistics)MicromechanicsFracture mechanicsStress concentrationStiffness matrixIntensity (physics)MechanicsComposite materialGeometryMathematicsGeologyEngineeringPhysicsComposite numberMineralogyOptics

Abstract

fetched live from OpenAlex

The intent of this paper is to propose an engineering approach to estimate the stress intensity factor of a micro crack emerging from an inclusion in relation with the morphology of the inclusion and its relative stiffness with the matrix. A micromechanical model, based on the FEA (finite element analysis) of the behavior of cracks initiated at micro structural features such as inclusions, has been developed using LEFM (Linear Elastic Fracture Mechanics) to predict the stress intensity factor of a micro crack emerging from an inclusion. Morphology of inclusions has important connotations in the development of the analysis. Stress intensity factor has been estimated from the FEA model for different crack geometries. Metallographic analysis of inclusions has been carried out to evaluate the typical inclusion geometry. It also suggests that micro cracks less than 1 μm behave differently than larger cracks.

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 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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.076
Threshold uncertainty score0.148

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 head0.015
GPT teacher head0.265
Teacher spread0.250 · 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 teacher head, 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

Citations3
Published2019
Admission routes2
Has abstractyes

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