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Record W2922591106 · doi:10.1111/ffe.12996

Concurrent ratcheting and stress relaxation at the notch root of steel samples undergoing asymmetric tensile loading cycles

2019· article· en· W2922591106 on OpenAlexafffund
Atefeh Shekarian, A. Varvani‐Farahani

Bibliographic record

VenueFatigue & Fracture of Engineering Materials & Structures · 2019
Typearticle
Languageen
FieldEngineering
TopicHigh Temperature Alloys and Creep
Canadian institutionsToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceStructural engineeringHysteresisStress (linguistics)Stress relaxationStress–strain curveLow-cycle fatigueComposite materialMechanicsDeformation (meteorology)CreepEngineeringPhysics

Abstract

fetched live from OpenAlex

Abstract The current study intends to develop a framework model to assess ratcheting and stress relaxation at the notch root of 1045 steel samples over asymmetric loading cycles. The framework involves the Ahmadzadeh‐Varvani (A‐V) kinematic hardening rule to control ratcheting progress and Neuber rule to accommodate for local stress and strain components at the vicinity of notch root. Plastic strain at notch root was first coupled with its counterpart in the A‐V model to establish a relation between local stress and backstress components. Calculated local stress and strain values at turning points enabled the A‐V model to assess ratcheting strain over each loading cycle. The stepwise drop in stresses at peak‐valley tips of hysteresis loops at the notch root was associated to coupled framework of the A‐V model and Neuber rule through constancy in local strain while ratcheting progressed over each cycle. This relaxed out the local stresses at tips of hysteresis loops to position on Neuber hyperbolic curve. Predicted ratcheting values at notch root of various diameters closely agreed with those of measured in steel samples over stress cycles.

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 score0.229
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.007
GPT teacher head0.211
Teacher spread0.203 · 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

Citations13
Published2019
Admission routes2
Has abstractyes

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