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Tensile Properties and Microstructure of Joined Vacuum Die Cast Aluminum Alloy A356 (T6) and Wrought Alloy 6061

2014· article· en· W1966766679 on OpenAlexaff
Meng Wang, Yan Zou, Henry Hu, Gary Meng, Patrick Cheng, Yeou li Chu

Bibliographic record

VenueAdvanced materials research · 2014
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloys Composites Properties
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsMaterials scienceMicrostructureMetallurgyAlloyUltimate tensile strengthIntermetallicGas metal arc welding6063 aluminium alloyScanning electron microscopeWeldingFiller metalComposite materialHeat-affected zoneArc welding

Abstract

fetched live from OpenAlex

In the present work, fusion-joining of vacuum high pressure die cast (HPDC) aluminum alloy A356 and wrought alloy 6061 by applying Gas Metal Arc Welding (GMAW-MIG) process was investigated to understand the effect of the MIG process on the microstructure and tensile behaviors of the base joined alloys (T6 Heat treatment A356 and 6061). The microstructures of the base metal (T6 heat treatment A356 and 6061), Heat Affected Zone (HAZ) and Fusion Zone (filler metal ER4043) were analyzed by Scanning Electron Microscopy (SEM) and optical microscopy. The results of tensile testing indicated that, the ultimate tensile strength (UTS) and yield strength (YS) of V-HPDC alluminium A356 subjected to T6 thermal treatment were relatively low, compared to both wrought alloy 6061 and the filler metal (ER 4043). The microstructure analysis showed that the low strengths of T6 A356 alloy should be at least attributed to the absence of the magnesium-based intermetallic phase, coarse grain structure and the presence of porosity, which resulted from the HPDC process, MIG welding and thermal treatment.

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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.031
GPT teacher head0.259
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

Citations8
Published2014
Admission routes1
Has abstractyes

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