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Role of Nb in 13Cr super-martensitic stainless steel

2013· article· en· W2093886857 on OpenAlexafffund
Xiaoping Ma, Cheng Zhou, Lijun Wang, Chunming Liu, Sundaresa Subramanian, Mariana Perez de Oliveira

Bibliographic record

VenueRem Revista Escola de Minas · 2013
Typearticle
Languageen
FieldEngineering
TopicMicrostructure and Mechanical Properties of Steels
Canadian institutionsMcMaster University
FundersCompanhia Brasileira de Metalurgia e MineraçãoChina Scholarship CouncilMcMaster University
KeywordsMaterials scienceVolume fractionMetallurgyAusteniteMicrostructureMartensiteDuctility (Earth science)ToughnessMartensitic stainless steelComposite materialCreep

Abstract

fetched live from OpenAlex

The effect of Nb microalloying on structure and physical properties of quenched and tempered 13%Cr martensitic stainless steel was investigated. Excellent strength and adequate toughness properties were obtained by 0.10 wt% Nb addition to low interstitial (N 0.01wt%, C < 0.02wt%) steel. The effect of Nb in 13%Cr steels with high N content was also studied in a commercial martensitic stainless steel sample containing higher levels of N and also alloyed with V. The microstructure, precipitate morphology and dispersion and volume fraction of reverse austenite were characterized. The strength properties obtained in the steel with 0.10%Nb are significantly higher than those of the V-containing steel. The study shows that whereas amount, size and dispersion of precipitates of microalloying elements contribute to enhanced strength, the optimum volume fraction of reverse austenite formed contributes to enhanced ductility and toughness properties. More importantly, high Nb additions to low N -13%Cr-1%Mo steel are found to improve significantly resistance against pitting corrosion significantly.

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 score0.186
Threshold uncertainty score0.632

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 head0.004
GPT teacher head0.172
Teacher spread0.168 · 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

Citations7
Published2013
Admission routes2
Has abstractyes

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