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Research on the Reverse Loading Hardening Model of the X80 Pipeline Steel

2013· article· en· W2035074151 on OpenAlexaff
Xing Zhou Cai, Lin Feng Wang, Shi Yan Zhao, Bao Feng Guo, Yu Xin Zhu

Bibliographic record

VenueAdvanced materials research · 2013
Typearticle
Languageen
FieldEngineering
TopicMetallurgy and Material Forming
Canadian institutionsMD Precision (Canada)
Fundersnot available
KeywordsHardening (computing)Materials scienceSofteningStrain hardening exponentMetallurgyComposite material

Abstract

fetched live from OpenAlex

Based on the reverse uniaxial loading test, the influences of the plastic deformation on the strength properties are studied. The softening of the X80 pipeline steel is observed which reveals the hardening type is mixed hardening. The expression of mixed hardening factor M and the material parameters of the nonlinear mixed hardening model under different M can be obtained. The results show that, if the pre-strain increases and ranges from 0.55% to 2.5%, the mixed hardening characteristics of the X80 pipeline steel will be more obvious and its M will also increase.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.018
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
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 science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.123
GPT teacher head0.360
Teacher spread0.236 · 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

Citations2
Published2013
Admission routes1
Has abstractyes

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