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Record W910641959 · doi:10.2172/1131409

Lost Foam Thin Wall: Feasibility of Producing Lost Foam Castings in Aluminum and Magnesium Based Alloys

2014· report· en· W910641959 on OpenAlexaff
Yemi Fasoyinu, Jon Griffin

Bibliographic record

Venuenot available
Typereport
Languageen
FieldEngineering
TopicAluminum Alloy Microstructure Properties
Canadian institutionsNatural Resources Canada
FundersU.S. Department of Energy
KeywordsMaterials scienceTearingCastingMetal foamAluminiumMetallurgyMagnesiumComposite materialYield (engineering)AlloyMolding (decorative)Sand castingMold

Abstract

fetched live from OpenAlex

With the increased emphasis on vehicle weight reduction, production of near-net shape components by lost foam casting will make significant inroad into the next-generation of engineering component designs. The lost foam casting process is a cost effective method for producing complex castings using an expandable polystyrene pattern and un-bonded sand. The use of un-bonded molding media in the lost foam process will impose less constraint on the solidifying casting, making hot tearing less prevalent. This is especially true in Al-Mg and Al-Cu alloy systems that are prone to hot tearing when poured in rigid molds partially due to their long freezing range. Some of the unique advantages of using the lost foam casting process are closer dimensional tolerance, higher casting yield, and the elimination of sand cores and binders. Most of the aluminum alloys poured using the lost foam process are based on the Al-Si system. Very limited research work has been performed with Al-Mg and Al-Cu type alloys. With the increased emphasis on vehicle weight reduction, and given the high-strength-to-weight-ratio of magnesium, significant weight savings can be achieved by casting thin-wall (≤ 3 mm) engineering components from both aluminum- and magnesium-base alloys.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.222
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.028
GPT teacher head0.246
Teacher spread0.217 · 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.

Study designObservational
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

Citations1
Published2014
Admission routes1
Has abstractyes

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