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Effect of Thermal Cycle on Microstructure and Corrosion Behavior of Duplex Stainless Steel SAF 2205 Electron Beam Welded Joint

2018· article· en· W2899753394 on OpenAlexaff
S. M. Khafagy, Morsy Amin Morsy, H.M. El Sherbini, Yosry F. Barakat

Bibliographic record

VenueKey engineering materials · 2018
Typearticle
Languageen
FieldMaterials Science
TopicHydrogen embrittlement and corrosion behaviors in metals
Canadian institutionsCanadian MPS Society for Mucopolysaccharide and Related Diseases
Fundersnot available
KeywordsMaterials scienceMicrostructureCorrosionMetallurgyVolume fractionAlloyAusteniteBase metalWeldingComposite material

Abstract

fetched live from OpenAlex

It is known that heat treatment (HT) highly affects the properties of base metal (BM) and fusion zones (FZ) of duplex stainless steel (DSS). In fact, it may give unwanted structure changes. Duplex stainless steels SAF 2205 welded joint was subjected to thermal cycle at temperature of 850◦C at holding times 1, 3, 5 and 7 hours. The influence of heating cycles and concentration of corrosive medium on the corrosion properties and microstructure of 2205 alloy was the objective of this work. It was found that process led to noticeable decrease in the corrosion resistance of BM and FZ specimens; moreover the decrease was large in BM than FZ. It was also found that sigma phase (σ) precipitated in the different zones of the structure. σ phase volume fraction was found to increase with increasing the holding time of HT, and its increase is larger in BM. Corrosion resistance was found to be oppositely related to σ phase formation. Secondary austenite phase (γ2) was also precipitated and its volume fraction in FZ was found to increase with increasing the holding time of HT and decreased in BM.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.006
GPT teacher head0.233
Teacher spread0.227 · 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 source (direct Gemma or distilled Codex), 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

Citations3
Published2018
Admission routes1
Has abstractyes

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