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Triphasic ratcheting strain prediction of materials over stress cycles

2012· article· en· W1922006128 on OpenAlexaff
G.R. Ahmadzadeh, A. Varvani‐Farahani

Bibliographic record

VenueFatigue & Fracture of Engineering Materials & Structures · 2012
Typearticle
Languageen
FieldEngineering
TopicHigh Temperature Alloys and Creep
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsMaterials scienceSofteningStructural engineeringHardening (computing)Nonlinear systemStress (linguistics)Deformation (meteorology)Composite materialMetallurgyEngineering

Abstract

fetched live from OpenAlex

ABSTRACT This study intends to formulate ratcheting strain evolution in steel alloys of 42CrMo, 20CS, SA333 Gr. 6 C‐Mn and OFHC copper over uniaxial stress cycles. Stages of ratcheting deformation were related to stress cycles, lifespan, mechanical properties and amplitude and mean stress components by means of linear and nonlinear functions. Terms of mechanical properties in the ratcheting formulation enabled to characterize ratcheting response of various materials over life cycles. These terms were further employed to interpret the influence of softening/hardening response of materials on ratcheting deformation. Ratcheting data for 42CrMo, 20CS, SA333 steels and OFHC copper reported in the literature were employed to evaluate the proposed ratcheting formulation. The predicted ratcheting strain values based on the proposed equation were found in good agreements as compared with the experimental data.

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 categoriesMeta-epidemiology (narrow)
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 score0.115
Threshold uncertainty score1.000

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)0.0010.000

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 head0.008
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.

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

Citations18
Published2012
Admission routes1
Has abstractyes

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