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Record W1748978898 · doi:10.1177/0021998315604037

Ratcheting prediction of Al 6061/SiC <sub>P</sub> composite samples under asymmetric stress cycles by means of the Ahmadzadeh–Varvani hardening rule

2015· article· en· W1748978898 on OpenAlexaff
G.R. Ahmadzadeh, A. Varvani‐Farahani

Bibliographic record

VenueJournal of Composite Materials · 2015
Typearticle
Languageen
FieldEngineering
TopicHigh Temperature Alloys and Creep
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsMaterials scienceShakedownComposite numberComposite materialHardening (computing)Strain hardening exponentStress (linguistics)Structural engineeringFinite element method

Abstract

fetched live from OpenAlex

The present study intends to examine ratcheting response of SiC P particle-reinforced Al 6061 matrix (Al 6061/SiC P ) composite samples over asymmetric load cycles based on the kinematic hardening rule of Ahmadzadeh–Varvani. The Ahmadzadeh–Varvani hardening rule offered a simple framework by taking into account of both material and stress level dependent coefficients to predict the quasi-shakedown of ratcheting of composite samples with various volume fractions under single- and multi-step loading conditions. The coefficients in the Ahmadzadeh–Varvani rule were estimated by means of mathematical expressions involving material properties, mean stress, and stress amplitude for any given stress levels. Ratcheting strain progressively increased as composite materials experienced low–high loading sequences when stress level increased over steps of loading histories. Ratcheting strain curves of low-high and high-low loading histories were successfully predicted. The predicted ratcheting curves with high–low loading sequences have shown change in ratcheting direction consistent 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)0.001
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 score0.031
Threshold uncertainty score0.776

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.012
GPT teacher head0.208
Teacher spread0.196 · 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

Citations11
Published2015
Admission routes1
Has abstractyes

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