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Record W2800143658 · doi:10.1139/tcsme-2017-1003

MECHANICAL CHARACTERIZATION OF NOTCHED HOT ROLLED MULTI-PHASE STEEL

2017· article· en· W2800143658 on OpenAlexaffvenue
Ke Niu, Abdolhamid Akbarzadeh, Zengtao Chen

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2017
Typearticle
Languageen
FieldEngineering
TopicStructural Load-Bearing Analysis
Canadian institutionsMcGill UniversityUniversity of Alberta
Fundersnot available
KeywordsFlangingMaterials scienceFinite element methodUltimate tensile strengthEnhanced Data Rates for GSM EvolutionDeformation (meteorology)MicrostructureDigital image correlationStress (linguistics)Structural engineeringComposite materialMetallurgyEngineering

Abstract

fetched live from OpenAlex

This paper presents a series of experimental and numerical studies on Hot Rolled Stretch Flanging steels. This study focuses on four prototyped Hot-Rolled Stretch Flanging steels (HR780SF). Circular- notched sheet steel samples are used to induce different stress triaxiality levels in the rolling direction of sheet materials. Digital image correlation technique measures the local true strain during the deformation process of the notched samples in uniaxial tensile test. The microstructure of the notched samples is examined to evaluate the effect of geometrical features of circular notches on the microstructural evolution during the plastic deformation. Finally, the numerical results obtained via a finite element simulation are validated by the collected experimental data. Our experimental studies reveal the possibility of crack formation along the width of HR780SF steels during the mechanical load. The crack formation, which deteriorates the structural performance of hot rolled steels, can be avoided by the heat treatment of samples prior to the mechanical tests. In addition, it is found that the effect of notch geometry on the stress state is much more considerable at the notch edge than the notch center.

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: none
Teacher disagreement score0.546
Threshold uncertainty score0.729

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.017
GPT teacher head0.235
Teacher spread0.218 · 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
Published2017
Admission routes2
Has abstractyes

Explore more

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