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Record W2006132537 · doi:10.1063/1.4850043

Prediction of edge failure of dual phase 780 steel subjected to hole expansion

2013· article· en· W2006132537 on OpenAlexafffund
David Anderson, C. Butcher, Michael J. Worswick

Bibliographic record

VenueAIP conference proceedings · 2013
Typearticle
Languageen
FieldEngineering
TopicFatigue and fracture mechanics
Canadian institutionsUniversity of Waterloo
FundersOffice of International Science and EngineeringNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsMaterials scienceShearing (physics)Composite materialUltimate tensile strengthFlangeStructural engineeringEnhanced Data Rates for GSM EvolutionConical surfaceDisplacement (psychology)Engineering

Abstract

fetched live from OpenAlex

The edge failure of a dual phase steel, DP780, under hole expansion (stretch flange) deformation conditions is examined. In order to assess the effect of damage processes without the presence of notches due to shearing, machined holes with a smooth edge condition were expanded with a conical punch. Images of the expansion as well as punch load-displacement were recorded during the tests. The GISSMO damage based failure criteria was implemented in a finite element model to predict the load-displacement of the punch and the initial crack formation. This failure criteria was calibrated using the effective plastic failure strain versus triaxiality data obtained from a set of uniaxial and notched tensile samples. The predicted punch load-displacement curve was found to be in agreement with observations including the load drop at the onset of failure. The predicted peak load of 23.4kN compared favorably to the observed peak of 24.4kN. The model predicted a hole expansion ratio of 53.4% and compared well to the experimentally observed ratio of 51.0% ± 10.0%.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.067
Threshold uncertainty score0.579

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 head0.021
GPT teacher head0.222
Teacher spread0.201 · 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

Citations6
Published2013
Admission routes2
Has abstractyes

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