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Record W2810947339 · doi:10.1007/s41230-018-8007-4

Niobium addition effect in molds at last cooling step on EN-GJL250 gray cast iron: Microstructural changes and electrochemical behavior

2018· article· en· W2810947339 on OpenAlexaff
Mohammed Oubelkacem Azzoug, Naïma Boutarek-Zaourar, D. Aboudi, Noureddine Madaoui, E. Mossang, Samir Mourad Chentouf

Bibliographic record

VenueChina Foundry · 2018
Typearticle
Languageen
FieldMaterials Science
TopicMetal Alloys Wear and Properties
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsMaterials scienceMicrostructureNiobiumCast ironMetallurgyGraphiteCorrosionElectrochemistryCastingParticle sizeComposite materialElectrodeChemical engineering

Abstract

fetched live from OpenAlex

The purpose of this study was to determine the impact of niobium addition as an inoculation element on the microstructure and electrochemical properties of EN-FGL250 gray cast iron. Niobium additions are in a powder form and have a 0.5 mm particle size at dfferent proportions of 1wt.% and 3wt.%. The addition was done during casting of the metal in the mold at the last cooling step of the melt cast iron. These additions have a significant impact on the phenomenon of solidification as the metal powder deposited in the sand molds creates new centers of germination and absorbs a lot of heat. The cooling rate directly affects the microstructure and electrochemical behavior. This is confirmed by SEM observations and electrochemical tests. Furthermore, the addition of niobium transforms the microstructure of gray cast iron from cellular structure into totally dendritic structure. As a consequence, the niobium addition affected the shape and size of graphite, thus considerably reducing the corrosion current density by increasing the polarization resistance R p .

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 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 score0.036
Threshold uncertainty score0.634

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.008
GPT teacher head0.237
Teacher spread0.229 · 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

Citations4
Published2018
Admission routes1
Has abstractyes

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