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Record W4386327513 · doi:10.3390/ma16175968

Influence of Oxygen Content in the Protective Gas on Pitting Corrosion Resistance of a 316L Stainless Steel Weld Joint

2023· article· en· W4386327513 on OpenAlexafffund
Mohammad Maroufkhani, Soroosh Hakimian, Alireza Khodabandeh, Iulian Radu, Lucas A. Hof, Mohammad Jahazi

Bibliographic record

VenueMaterials · 2023
Typearticle
Languageen
FieldMaterials Science
TopicHydrogen embrittlement and corrosion behaviors in metals
Canadian institutionsPCL Construction (Canada)École de Technologie Supérieure
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCorrosionMetallurgyMaterials scienceWeldingOxygenPitting corrosionGas tungsten arc weldingOxideLimiting oxygen concentrationLayer (electronics)Composite materialArc weldingChemistry

Abstract

fetched live from OpenAlex

Gas tungsten arc welding (GTAW) is commonly used for joining pipelines; however, it often leads to discoloration in the heat-affected zone (HAZ). In this study, 316L pipes were welded with different concentrations of oxygen present in the argon purge gas during welding. The objective of this study was to investigate the effect of oxygen concentration in the protective gas on the pitting corrosion resistance of welded pipes. The experimental results showed that the thickness of the oxide layer formed in the HAZ depends on the concentration of oxygen in the protective gas. Increasing the oxygen concentration in the protective gas resulted in an increase in pitting corrosion resistance until a critical value, beyond which the resistance decreased. The results showed that the thickness of the oxide layer formed in the HAZ depends on the concentration of oxygen in the protective gas. Increasing the oxygen concentration in the protective gas increased the pitting corrosion resistance until a critical value, beyond which the resistance decreased due to the formation of iron oxide. This study provides valuable insights for improving the corrosion resistance of welded pipes in the oil and gas industry.

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 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 score0.019
Threshold uncertainty score0.487

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 communication0.0000.000
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 head0.051
GPT teacher head0.277
Teacher spread0.226 · 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

Citations10
Published2023
Admission routes2
Has abstractyes

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