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Record W3121971480 · doi:10.4271/2021-01-0281

Thermo-Mechanical Fatigue (TMF) Life of Ductile SiMo Cast Iron with Aluminum Addition

2021· article· en· W3121971480 on OpenAlexaff
Guangchun Quan, Delin Li, Xijia Wu

Bibliographic record

VenueSAE technical papers on CD-ROM/SAE technical paper series · 2021
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloy Microstructure Properties
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsAluminiumCast ironMaterials scienceMetallurgyDuctile iron

Abstract

fetched live from OpenAlex

<div class="section abstract"><div class="htmlview paragraph">Strain controlled thermo-mechanical fatigue (TMF) tests were conducted on a high Silicon ductile cast iron (SiMo) as the baseline material and a similar SiMo cast iron with aluminum addition (SiMoAl). The much improved fatigue life with aluminum addition is analyzed using the integrated creep-fatigue theory (ICFT) in combination with the metallurgical analysis on the tested coupons. Addition of about 3 wt.% Aluminum significantly improved TMF life of the SiMo cast iron. The results are explained by elimination of brittleness at middle temperature range, the higher flow stress, lower creep rate and higher oxidation resistance from Al addition.</div></div>

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.922
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.011
GPT teacher head0.209
Teacher spread0.198 · 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

Citations3
Published2021
Admission routes1
Has abstractyes

Explore more

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