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Record W3117893990 · doi:10.3390/met11010003

Effect of Laser Power on Microstructure and Micro-Galvanic Corrosion Behavior of a 6061-T6 Aluminum Alloy Welding Joints

2020· article· en· W3117893990 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

VenueMetals · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvanced Welding Techniques Analysis
Canadian institutionsWestern University
FundersNational Natural Science Foundation of China
KeywordsMaterials scienceMicrostructureWeldingMetallurgyAlloyGalvanic corrosionIntermetallicCorrosionLaser beam weldingGalvanic cellScanning electron microscopeGas metal arc weldingPitting corrosionAluminiumComposite material

Abstract

fetched live from OpenAlex

The 6061-T6 aluminum alloy welding joints were fabricated using gas metal arc welding (GMAW) of various laser powers, and the effect of laser power on the microstructure evolution of the welding joints was investigated. The corrosion behaviors of 6061-T6 aluminum alloy welding joints were investigated in 3.5 wt% NaCl solution using scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDS). The results showed that the micro-galvanic corrosion initiation from Mg2Si or around the intermetallic particles (Al-Fe-Si) is observed after the immersion test due to the inhomogeneous nature of the microstructure. The preferential dissolution of the Mg2Si and Al-Fe-Si is believed to be the possible cause of pitting corrosion. When the laser power reached 5 kW, the microstructure of the welded joint mainly consisted of Al-Fe-Si rather than the Mg2Si at 2 kW. The relatively higher content of Al-Fe-Si with increasing in laser power would increase the volume of corrosion pits.

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 score0.005
Threshold uncertainty score0.731

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.241
Teacher spread0.233 · 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