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Record W2617737941 · doi:10.11159/mmme17.111

Forming Gears from ZA-27 Zinc Alloy Using Semi-Solid Slurry SqueezeCasting Process

2017· article· en· W2617737941 on OpenAlexvenueno aff
Thawatchai Plookphol, Somjai Janudom, Supakit Vongcharoenpon

Bibliographic record

VenueProceedings of the World Congress on Mechanical, Chemical, and Material Engineering · 2017
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloy Microstructure Properties
Canadian institutionsnot available
Fundersnot available
KeywordsSlurryAlloyMetallurgyCastingMaterials scienceZincProcess (computing)Computer scienceComposite material

Abstract

fetched live from OpenAlex

Semi-solid metal processing has been developed for years.The semi-solid metal processing can be classified into two types, thixoforming and rheocasting.In recent years, the rheocasting process gains more attention from the industries since it has a lower capital cost and materials cost.Several rheocasting techniques have been invented for preparing semi-solid metal slurries for casting.Gas Induced Semi-Solid (GISS) is a new semi-solid slurry preparation technique recently developed [1].In the GISS technique, fine inert gas bubbles are injected through a graphite diffuser into the molten alloy, causing a vigorous agitation and a rapid heat extraction from the molten metal, at early stage of solidification the semisolid structure is formed.The GISS technique can be used with many alloys to create semi-solid slurries, such as aluminum alloys, zinc alloys and tin alloys, and it can be used with conventional casting processes, such as die casting, squeeze casting and gravity casting [2,3].The purpose of present study is to evaluate a possibility of using the GISS technique and squeeze casting process for producing gears from a high strength Zn-Al alloy.Commercial ZA-27 zinc alloy with nominal chemical compositions of 27-28 wt.% Al, 2.0-2.5 wt.% Cu and the remaining Zn, was used in this study.Effects of rheocasting time and preheating die temperature on the completeness of gear specimens were evaluated.Nitrogen gas was used and the gas bubbles were introduced into molten ZA-27 alloy through a porous graphite diffuser at temperature 510 C for 10 and 15 s.Two preheating die temperatures, 270 and 300 C were used for preheating gear die set before squeeze casting was commenced.Semi-solid alloy slurry was squeezed in the die with a hydraulic compression pressure of 100 MPa for 30 s. Microstructural analysis and hardness testing were performed on the as-cast gear specimens.It was found that using rheocasting time both 10 and 15 s and preheating die temperature 270 C cannot produce complete gears.Some gear teeth were not completely formed since the cooling rate was too fast and solidification was completed before the alloy slurry could fill up the gear teeth die space.Complete gear specimens were successfully achieved when using rheocasting time 15 s and preheating die temperature 300 C.Average hardness of as-cast gear teeth and gear body was 68.3±2.7 and 69.6±1.1 HRB, respectively.Microstructures of gear teeth and gear body consisted of globular Al-rich α phase, surrounded by β, η and ε phases.Segregation with dendritic structure was formed on the rim of some gear teeth, with thickness of 0.5-1 mm.From the present study it can be concluded that it is possible to form ZA-27 zinc alloy gears using the GISS slurry squeeze casting process by appropriate control process parameters: the rheocasting temperature, the rheocasting time, and the preheating die temperature.

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
Bibliometrics0.0000.000
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 head0.011
GPT teacher head0.224
Teacher spread0.213 · 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".

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Citations0
Published2017
Admission routes1
Has abstractyes

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