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Record W2014636542 · doi:10.1111/jace.13230

Quantitative Characterization of Mechanical Stress Field and Fracture Strength in Isotropic Brittle Materials During Crack Tip Propagation

2014· article· en· W2014636542 on OpenAlexaff
Roberto Dugnani, Ricardo J. Zednik

Bibliographic record

VenueJournal of the American Ceramic Society · 2014
Typearticle
Languageen
FieldEngineering
TopicRock Mechanics and Modeling
Canadian institutionsÉcole de Technologie SupérieureUniversité du Québec à Montréal
Fundersnot available
KeywordsMaterials scienceFlexural strengthIsotropyStress intensity factorBrittlenessFracture mechanicsComposite materialFracture (geology)Stress fieldCharacterization (materials science)Stress (linguistics)StiffnessBendingFracture toughnessStructural engineeringFinite element methodOpticsPhysics

Abstract

fetched live from OpenAlex

A generalized method for characterizing mechanical stresses in brittle materials during crack tip propagation is presented. This approach was derived from classical fracture mechanics and is therefore mechanism independent and applies to any isotropic brittle material, including glasses, fine grained ceramics or metals, and high stiffness polymers. A practical implementation demonstrates the merits of this technique: the fracture strength can be determined by characterizing the angle between the free surface of a flexural overload fracture and stress intensity factor loci. The accuracy of this method was phenomenologically validated using flexural strength tests on glass as a model material system. In addition, such fractographic measurements can also be used to characterize an inhomogeneous internal stress field, and thereby, for example, help discriminate whether the sample failed due to pure bending loads alone, or whether membrane stresses were also present.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.006
GPT teacher head0.218
Teacher spread0.213 · 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

Citations4
Published2014
Admission routes1
Has abstractyes

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Same venueJournal of the American Ceramic SocietySame topicRock Mechanics and ModelingFrench-language works237,207