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Record W3093108467 · doi:10.5006/3604

Local Electrochemical Behavior of Friction Stir Welded Mg-Al-Mn Alloy Joints

2020· article· en· W3093108467 on OpenAlexaff
S.H.S. Salleh, Zhuoran Zeng, Elizabeth A. McNally, N. Birbilis, Mustafa Musameh, Kishore Venkatesan, J.R. Kish, Joseph R. McDermid, Carol Frances Glover, G. Williams, S. Thomas

Bibliographic record

VenueCORROSION · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvanced Welding Techniques Analysis
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMaterials scienceScanning electrochemical microscopyCathodic protectionElectrochemistryCorrosionFriction stir weldingElectrolyteMicrostructureGalvanic cellAnodeMetallurgyAlloyWeldingElectrodeChemistry

Abstract

fetched live from OpenAlex

Local scanning electrochemical microscopy (SECM), along with a scanning vibrating electrode technique (SVET), were used to spatially map anodic and cathodic regions in a complex assembly including a friction stir lap welded joint between two Mg-Al-Mn alloys, namely AM60 and AM30. The assembly investigated herein was comprised of the two base materials (AM60 and AM30) and a stir zone (SZ). The transitory electrochemical characteristics of the three regions were perceived from different electrochemical tests and have been correlated to their respective microstructures. Potentiometric and voltametric SECM performed on the assembly in 0.01 M NaCl revealed that during the first few minutes of exposure, AM60 and SZ regions act as dominant anodic regions in the assembly while after 2 h of exposure these regions transition to become cathodic relative to the AM30 region. Galvanic interaction between the different weld regions immersed in a significantly more conductive NaCl electrolyte was investigated by SVET. Under such conditions, filiform-like corrosion initiated, and propagated exclusively within the AM30 region, resulting in cathodic activation of the corroded surface. However, after approximately 5 h of exposure, the AM60 region preferentially dissolves following sufficient cathodic activation of the AM30 region.

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: none
Teacher disagreement score0.787
Threshold uncertainty score0.540

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 head0.012
GPT teacher head0.237
Teacher spread0.225 · 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
Published2020
Admission routes1
Has abstractyes

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Same venueCORROSIONSame topicAdvanced Welding Techniques AnalysisFrench-language works237,207