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Record W2557931181 · doi:10.1115/pvp2016-63831

Critical Strain and Damage Evolution for Crack Growth From a Sharp Notch Tip of High-Strength Steel

2016· article· en· W2557931181 on OpenAlexafffund
Feng Yu, P.‐Y. Ben Jar, Michael T. Hendry

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicStructural Load-Bearing Analysis
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaAlberta Innovates - Technology FuturesUniversity of AlbertaTransport Canada
KeywordsMaterials scienceFracture toughnessUltimate tensile strengthStructural engineeringFinite element methodEnhanced Data Rates for GSM EvolutionFracture (geology)Stress (linguistics)Deformation (meteorology)ToughnessModulusComposite materialComputer science

Abstract

fetched live from OpenAlex

This paper proposes details of an approach that uses expressions of fracture strain and damage evolution as functions of stress triaxiality for notch-free specimens to predict their values for crack growth from a sharp notch tip of a single-edge-notched bend (SENB) specimen. Experimental testing and finite element (FE) modelling are used to determine the basic mechanical properties and deformation behaviour of those specimens, which are needed to calibrate model constants in the proposed approach and to validate prediction from the approach. Three types of mechanical testing were conducted, using standard smooth tensile, short-gauged tensile and standard SENB specimens. The FE modeling is to establish constitutive relationship between stress and strain for notch-free specimens so that the FE modeling can be used to determine parameters such as stress triaxiality and unloading modulus for the prediction of fracture strain and damage evolution at the sharp notch tip of SENB specimen. The study will then examine whether the proposed approach can predict the trend of variation for fracture toughness among three high-strength steels, which is an on-going study and the results will be presented in the conference.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.630
Threshold uncertainty score0.342

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 head0.008
GPT teacher head0.217
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 teacher head, 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

Citations0
Published2016
Admission routes2
Has abstractyes

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