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Record W2622295088 · doi:10.1080/2374068x.2017.1336352

Interfacial heat transfer of squeeze casting of wrought aluminum alloy 5083 with variation in wall thicknesses

2017· article· en· W2622295088 on OpenAlexafffund
Xuezhi Zhang, Fang Li, Henry Hu, Xueyuan Nie, Jimi Tjong

Bibliographic record

VenueAdvances in Materials and Processing Technologies · 2017
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloy Microstructure Properties
Canadian institutionsUniversity of Windsor
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Windsor
KeywordsMaterials scienceAlloyCastingForgingDie castingAluminiumMetallurgyComposite materialDie (integrated circuit)MoldHeat transferHeat transfer coefficientMechanics

Abstract

fetched live from OpenAlex

The squeeze casting technique, combining the advantage of die casting and forging process, is becoming one of the fast growing and efficient methods for production of near-net to net shape of aluminum alloys. Wrought aluminum alloys with their high strength properties have achieved widespread use in the automotive industry. In this study, a five-step squeeze casting experiment using aluminum wrought alloy 5083 has been designed and conducted. The temperature profiles inside the die and casting were measured. The metal/die interfacial heat transfer coefficient (IHTC) by using a computer program based on the inverse method was calculated and studied. The results showed that the IHTC of the thicker section had a higher peak value than the thinner one in which a firm contact might be formed at the metal/die interface as the section became thicker, which facilitated the transfer of the applied hydraulic pressure to the metal/die interface. The IHTC value increased immediately after the mold cavity was filled by the liquid metal, and decreased as the solidification process proceeded. The regression analysis indicated that an empirical equation, relating IHTCs to the wall thickness of the casting could be established based on a logarithmic function.

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 score0.025
Threshold uncertainty score0.528

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 communication0.0000.001
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 head0.008
GPT teacher head0.226
Teacher spread0.218 · 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
Published2017
Admission routes2
Has abstractyes

Explore more

Same venueAdvances in Materials and Processing TechnologiesSame topicAluminum Alloy Microstructure PropertiesFrench-language works237,207