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Record W2056369913 · doi:10.4271/2013-01-1026

Fusion Welding of Vacuum High Pressure Die Cast Aluminum Alloy A356 and Wrought Alloy 6061

2013· article· en· W2056369913 on OpenAlexafffund
Meng Wang, Henry Hu

Bibliographic record

VenueSAE International Journal of Materials and Manufacturing · 2013
Typearticle
Languageen
FieldEngineering
TopicAdvanced Welding Techniques Analysis
Canadian institutionsUniversity of Windsor
FundersUniversity of Windsor
KeywordsAlloyMetallurgyMaterials scienceDie (integrated circuit)AluminiumWelding

Abstract

fetched live from OpenAlex

<div class="section abstract"><div class="htmlview paragraph">Recently, joining of cast aluminum components with wrought and/or cast similar metals becomes an urgent task for the auto industry to develop light-weight complex and large-scale chassis and body structures for further reduction in vehicle weight. In this study, fusion-joining of vacuum high pressure die cast (VHPDC) alloy A356 subjected to T5 heat treatment and wrought alloy 6061 with the Gas Metal Arc Welding (GMAW-MIG) process was experimented in an effort to understand the effect of the MIG process on the microstructure development and tensile behaviors of the base alloys (T5 A356 and 6061), Heat Affected Zone (HAZ) and Fusion Zone (filler metal ER4043). The results of tensile testing indicated that the ultimate tensile strength (UTS), yield strength (YS) and elongation (E<sub>f</sub>) of VHPDC T5 A356 were relatively high, compared to both wrought alloy 6061 and the filler metal (ER 4043). The microstructure analysis showed that the improvement in the tensile properties of T5 A356 should be mainly attributed to the minimized porosity content, the presence of strengthening intermetallic phases and fine grain structure, which resulted from the combined effects of the applied manufacturing processes, i.e., the VHPDC, T5 heat treatment and MIG welding.</div></div>

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: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.570

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 head0.006
GPT teacher head0.218
Teacher spread0.212 · 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

Citations3
Published2013
Admission routes2
Has abstractyes

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