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Record W1983700855 · doi:10.4236/eng.2010.25051

Influence of Clad Metal Chemistry on Stress Corrosion Cracking Behaviour of Stainless Steels Claddings in Chloride Solution

2010· article· en· W1983700855 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEngineering · 2010
Typearticle
Languageen
FieldMaterials Science
TopicCorrosion Behavior and Inhibition
Canadian institutionsMitel (Canada)
FundersFundação de Amparo à Pesquisa do Estado de Minas GeraisPetrobrasConselho Nacional de Desenvolvimento Científico e Tecnológico
KeywordsStress corrosion crackingMetallurgyCorrosionMaterials scienceCladding (metalworking)CrackingAusteniteFerrite (magnet)Austenitic stainless steelMicrostructureMetalBoilingComposite materialChemistry

Abstract

fetched live from OpenAlex

The effect of clad metal composition on stress corrosion cracking (SCC) behavior of three types of SMAW filler metals (E308L-16, E309-16 and E316L-16), used for cladding components subjected to highly corrosive conditions, was investigated in boiling 43% MgCl2 solution. In order to evaluate the stress corrosion cracking susceptibility of the top layer, constant load tests and metallographic examinations in tested SCC specimens were conducted. The susceptibility to stress corrosion cracking was evaluated in terms of the time-to-fracture. Results showed that the E309-16 clad metal presented the best SCC resistance. This may be attributed to the presence of a discontinuous delta-ferrite network in the austenitic matrix, which acted as a barrier to cracks propagation. Concerning to E308-16 and E316L-16 clad metals, results showed that these presented a similar SCC test performance. Their higher SCC susceptibility may be attributed to the presence of continuous vermicular delta-ferrite in their microstructure.

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.

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 score0.027
Threshold uncertainty score0.557

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 head0.007
GPT teacher head0.227
Teacher spread0.220 · 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