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Influence of Aging Temperatures and Times on Mechanical Properties of Vacuum High Pressure Die Cast Aluminum Alloy A356

2012· article· en· W2086342432 on OpenAlexaff
Xue Zhi Zhang, Kazi Ahmmed, Meng Wang, Henry Hu

Bibliographic record

VenueAdvanced materials research · 2012
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloy Microstructure Properties
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsIntermetallicMaterials scienceAlloyEutectic systemMetallurgyAluminiumUltimate tensile strengthMagnesiumDie (integrated circuit)Magnesium alloyPhase (matter)Composite material

Abstract

fetched live from OpenAlex

In this study a number of thermal treatment schemes over a wide range of temperatures between 120˚ to 350˚ C and times (30 120 minutes) have been experimented in an effort to understand the effect of thermal treatment on tensile properties of vacuum die cast modified aluminum alloy A356. The results show that, the morphology of eutectic silicon has a sound effect on the tensile properties of the tested alloy. The content of magnesium-based intermetallic phases, their morphology and distribution throughout the matrix affect the mechanical properties of the aged alloy as well. The reduction in the strengths of the alloy treated at 350°C for two hours should be at least attributed partly to the absence of the magnesium-based intermetallic phase. However the presence of sufficient amount of magnesium intermetallic phase had played important role in strengthening the alloy thermally treated at 200°C for 90 minutes.

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 score0.004

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.021
GPT teacher head0.274
Teacher spread0.253 · 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

Citations15
Published2012
Admission routes1
Has abstractyes

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