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Record W2900704410 · doi:10.5539/jmsr.v8n1p1

Structure Property Correlation of Thin Wall Ductile Iron

2018· article· en· W2900704410 on OpenAlexvenueno aff
Rajat Upadhyaya, Kamlesh Kumar Singh

Bibliographic record

VenueJournal of Materials Science Research · 2018
Typearticle
Languageen
FieldEngineering
TopicMetallurgy and Material Forming
Canadian institutionsnot available
Fundersnot available
KeywordsMaterials scienceUltimate tensile strengthElongationDuctile ironIndentation hardnessVolume fractionBearing (navigation)Composite materialThin sectionMetallurgyMicrostructureMineralogy

Abstract

fetched live from OpenAlex

This research work concerns an assessment of the effect of one step inoculation method on thin section castings (2-5) mm analyzed. In this study inoculants representing Ce, Zr and Sr bearing Fe-Si alloys were used. It has been observed that section thickness of 2 mm thin plate containing Ce bearing Fe-Si alloys at 0.4wt% contain mainly pearlitic matrix having microhardness (HV20), ultimate tensile strength (UTS) and percentage of elongation are 272 HV, 720 MPa and 4.5% respectively. It was observed that wear of 2 mm section thickness of thin plate containing Ce bearing Fe-Si alloys have greater wear resistance (272HV) compare to Zr bearing Fe-Si alloys (262HV) and Sr bearing Fe-Si alloys (259HV) respectively at 0.4wt% due to reason of pearlitic and ferritic volume fraction contents present in matrix.

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)0.000
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 score0.004
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.046
GPT teacher head0.331
Teacher spread0.285 · 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
Published2018
Admission routes1
Has abstractyes

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